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@@ -77,5 +77,4 @@ HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
 
 # Run the FastAPI server
 # The module path is constructed to work with the /app/env structure
-ENV ENABLE_WEB_INTERFACE=true
 CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 8000"]

README.md

@@ -6,7 +6,6 @@ colorTo: yellow
 sdk: docker
 pinned: false
 app_port: 8000
-base_path: /web
 tags:
   - openenv
 ---

evals_hf.ipynb

@@ -26,10 +26,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "c3d4e5f6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imports OK\n"
+     ]
+    }
+   ],
    "source": [
     "import csv\n",
     "import json\n",
@@ -65,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4e5f6a7",
+   "id": "1f1d9271",
    "metadata": {},
    "source": [
     "## Cell 2 — Config  ✏️ Edit this cell to change eval settings"
@@ -73,10 +81,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "e5f6a7b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model      : Qwen/Qwen3-1.7B\n",
+      "4-bit      : False\n",
+      "Temperature: 0.3\n",
+      "Levels     : all 3 (easy, medium, hard)\n",
+      "Seeds      : [1, 2, 3]\n",
+      "Output dir : ./outputs/hf_evals\n"
+     ]
+    }
+   ],
    "source": [
     "# ── Model ─────────────────────────────────────────────────────────────────────\n",
     "MODEL_ID        = \"Qwen/Qwen3-1.7B\"   # HF model ID or local path / adapter dir\n",
@@ -123,10 +144,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a7b8c9d0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Difficulties to evaluate : ['easy', 'medium', 'hard']\n",
+      "Total episodes           : 9\n"
+     ]
+    }
+   ],
    "source": [
     "ALL_DIFFICULTIES: List[Dict[str, Any]] = [\n",
     "    {\"difficulty\": \"easy\",   \"max_steps\": 200, \"description\": \"1 fire source · slow spread · calm wind · high humidity\"},\n",
@@ -195,10 +225,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "c9d0e1f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HFChatModel class defined.\n"
+     ]
+    }
+   ],
    "source": [
     "class HFChatModel(SimpleChatModel):\n",
     "    \"\"\"\n",
@@ -308,10 +346,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "e1f2a3b4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prompt builder and action parser defined.\n"
+     ]
+    }
+   ],
    "source": [
     "def _build_user_message(obs: Dict[str, Any], history: List[str]) -> str:\n",
     "    \"\"\"Convert a raw observation dict + history into the LLM user message.\"\"\"\n",
@@ -466,10 +512,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "a3b4c5d6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Episode runner defined.\n"
+     ]
+    }
+   ],
    "source": [
     "def run_episode(\n",
     "    llm: HFChatModel,\n",
@@ -637,10 +691,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "c5d6e7f8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Server not reachable at http://localhost:8000: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /health (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mConnectionRefusedError\u001b[39m                    Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/connection.py:204\u001b[39m, in \u001b[36mHTTPConnection._new_conn\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    203\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m204\u001b[39m     sock = \u001b[30;43mconnection\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mcreate_connection\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    205\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_dns_host\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mport\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    206\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    207\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43msource_address\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msource_address\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    208\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43msocket_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msocket_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    209\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    210\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m socket.gaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/util/connection.py:85\u001b[39m, in \u001b[36mcreate_connection\u001b[39m\u001b[34m(address, timeout, source_address, socket_options)\u001b[39m\n\u001b[32m     84\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m85\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[32m     86\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m     87\u001b[39m     \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/util/connection.py:73\u001b[39m, in \u001b[36mcreate_connection\u001b[39m\u001b[34m(address, timeout, source_address, socket_options)\u001b[39m\n\u001b[32m     72\u001b[39m     sock.bind(source_address)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m \u001b[30;43msock\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mconnect\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43msa\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m     74\u001b[39m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n",
+      "\u001b[31mConnectionRefusedError\u001b[39m: [Errno 111] Connection refused",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[31mNewConnectionError\u001b[39m                        Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/connectionpool.py:787\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m    786\u001b[39m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m787\u001b[39m response = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_make_request\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    788\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mconn\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    789\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmethod\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    790\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    791\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtimeout_obj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    792\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mbody\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mbody\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    793\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    794\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mchunked\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mchunked\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    795\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mretries\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mretries\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    796\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mresponse_conn\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mresponse_conn\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    797\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpreload_content\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpreload_content\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    798\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdecode_content\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdecode_content\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    799\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mresponse_kw\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    800\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    802\u001b[39m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/connectionpool.py:493\u001b[39m, in \u001b[36mHTTPConnectionPool._make_request\u001b[39m\u001b[34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[39m\n\u001b[32m    492\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m493\u001b[39m     \u001b[30;43mconn\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mrequest\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    494\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mmethod\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    495\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    496\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mbody\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mbody\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    497\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    498\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mchunked\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mchunked\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    499\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mpreload_content\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpreload_content\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    500\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdecode_content\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdecode_content\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    501\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43menforce_content_length\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43menforce_content_length\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    502\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    504\u001b[39m \u001b[38;5;66;03m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[32m    505\u001b[39m \u001b[38;5;66;03m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[32m    506\u001b[39m \u001b[38;5;66;03m# With this behaviour, the received response is still readable.\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/connection.py:500\u001b[39m, in \u001b[36mHTTPConnection.request\u001b[39m\u001b[34m(self, method, url, body, headers, chunked, preload_content, decode_content, enforce_content_length)\u001b[39m\n\u001b[32m    499\u001b[39m     \u001b[38;5;28mself\u001b[39m.putheader(header, value)\n\u001b[32m--> \u001b[39m\u001b[32m500\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mendheaders\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    502\u001b[39m \u001b[38;5;66;03m# If we're given a body we start sending that in chunks.\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/http/client.py:1333\u001b[39m, in \u001b[36mHTTPConnection.endheaders\u001b[39m\u001b[34m(self, message_body, encode_chunked)\u001b[39m\n\u001b[32m   1332\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[32m-> \u001b[39m\u001b[32m1333\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_send_output\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mmessage_body\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mencode_chunked\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mencode_chunked\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/http/client.py:1093\u001b[39m, in \u001b[36mHTTPConnection._send_output\u001b[39m\u001b[34m(self, message_body, encode_chunked)\u001b[39m\n\u001b[32m   1092\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m._buffer[:]\n\u001b[32m-> \u001b[39m\u001b[32m1093\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msend\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mmsg\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1095\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1096\u001b[39m \n\u001b[32m   1097\u001b[39m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.12.12-linux-x86_64-gnu/lib/python3.12/http/client.py:1037\u001b[39m, in \u001b[36mHTTPConnection.send\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m   1036\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.auto_open:\n\u001b[32m-> \u001b[39m\u001b[32m1037\u001b[39m     \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mconnect\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1038\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/connection.py:331\u001b[39m, in \u001b[36mHTTPConnection.connect\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    330\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m     \u001b[38;5;28mself\u001b[39m.sock = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_new_conn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    332\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._tunnel_host:\n\u001b[32m    333\u001b[39m         \u001b[38;5;66;03m# If we're tunneling it means we're connected to our proxy.\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/connection.py:219\u001b[39m, in \u001b[36mHTTPConnection._new_conn\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    218\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m--> \u001b[39m\u001b[32m219\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m NewConnectionError(\n\u001b[32m    220\u001b[39m         \u001b[38;5;28mself\u001b[39m, \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFailed to establish a new connection: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    221\u001b[39m     ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n\u001b[32m    223\u001b[39m sys.audit(\u001b[33m\"\u001b[39m\u001b[33mhttp.client.connect\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mself\u001b[39m.host, \u001b[38;5;28mself\u001b[39m.port)\n",
+      "\u001b[31mNewConnectionError\u001b[39m: HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[31mMaxRetryError\u001b[39m                             Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/requests/adapters.py:645\u001b[39m, in \u001b[36mHTTPAdapter.send\u001b[39m\u001b[34m(self, request, stream, timeout, verify, cert, proxies)\u001b[39m\n\u001b[32m    644\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m645\u001b[39m     resp = \u001b[30;43mconn\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43murlopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    646\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mmethod\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrequest\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mmethod\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    647\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    648\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mbody\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrequest\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mbody\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    649\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrequest\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    650\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mredirect\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    651\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43massert_same_host\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    652\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mpreload_content\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    653\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdecode_content\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    654\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mretries\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mmax_retries\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    655\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    656\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mchunked\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mchunked\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    657\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    659\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/connectionpool.py:841\u001b[39m, in \u001b[36mHTTPConnectionPool.urlopen\u001b[39m\u001b[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[39m\n\u001b[32m    839\u001b[39m     new_e = ProtocolError(\u001b[33m\"\u001b[39m\u001b[33mConnection aborted.\u001b[39m\u001b[33m\"\u001b[39m, new_e)\n\u001b[32m--> \u001b[39m\u001b[32m841\u001b[39m retries = \u001b[30;43mretries\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mincrement\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    842\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmethod\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43merror\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mnew_e\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m_pool\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m_stacktrace\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43msys\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mexc_info\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m2\u001b[39;49m\u001b[30;43m]\u001b[39;49m\n\u001b[32m    843\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    844\u001b[39m retries.sleep()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/urllib3/util/retry.py:535\u001b[39m, in \u001b[36mRetry.increment\u001b[39m\u001b[34m(self, method, url, response, error, _pool, _stacktrace)\u001b[39m\n\u001b[32m    534\u001b[39m     reason = error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[32m--> \u001b[39m\u001b[32m535\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mreason\u001b[39;00m  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[32m    537\u001b[39m log.debug(\u001b[33m\"\u001b[39m\u001b[33mIncremented Retry for (url=\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[33m\"\u001b[39m, url, new_retry)\n",
+      "\u001b[31mMaxRetryError\u001b[39m: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /health (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[31mConnectionError\u001b[39m                           Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      5\u001b[39m     print(f\"Server health check PASSED  ({ENV_URL})\")\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m Exception \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m RuntimeError(f\"Server not reachable at {ENV_URL}: {exc}\")\n\u001b[32m      8\u001b[39m \n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/requests/api.py:73\u001b[39m, in \u001b[36mget\u001b[39m\u001b[34m(url, params, **kwargs)\u001b[39m\n\u001b[32m     63\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[32m     64\u001b[39m \n\u001b[32m     65\u001b[39m \u001b[33;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m     70\u001b[39m \u001b[33;03m:rtype: requests.Response\u001b[39;00m\n\u001b[32m     71\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mrequest\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mparams\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mparams\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/requests/api.py:59\u001b[39m, in \u001b[36mrequest\u001b[39m\u001b[34m(method, url, **kwargs)\u001b[39m\n\u001b[32m     58\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m sessions.Session() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43msession\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mrequest\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mmethod\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmethod\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43murl\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/requests/sessions.py:592\u001b[39m, in \u001b[36mSession.request\u001b[39m\u001b[34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[39m\n\u001b[32m    591\u001b[39m send_kwargs.update(settings)\n\u001b[32m--> \u001b[39m\u001b[32m592\u001b[39m resp = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msend\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mprep\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43msend_kwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    594\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/requests/sessions.py:706\u001b[39m, in \u001b[36mSession.send\u001b[39m\u001b[34m(self, request, **kwargs)\u001b[39m\n\u001b[32m    705\u001b[39m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m706\u001b[39m r = \u001b[30;43madapter\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msend\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mrequest\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    708\u001b[39m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/requests/adapters.py:678\u001b[39m, in \u001b[36mHTTPAdapter.send\u001b[39m\u001b[34m(self, request, stream, timeout, verify, cert, proxies)\u001b[39m\n\u001b[32m    676\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m SSLError(e, request=request)\n\u001b[32m--> \u001b[39m\u001b[32m678\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e, request=request)\n\u001b[32m    680\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ClosedPoolError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[31mConnectionError\u001b[39m: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /health (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      3\u001b[39m     health = requests.get(f\"{ENV_URL}/health\", timeout=\u001b[32m5\u001b[39m)\n\u001b[32m      4\u001b[39m     health.raise_for_status()\n\u001b[32m      5\u001b[39m     print(f\"Server health check PASSED  ({ENV_URL})\")\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m Exception \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m RuntimeError(f\"Server not reachable at {ENV_URL}: {exc}\")\n\u001b[32m      8\u001b[39m \n\u001b[32m      9\u001b[39m \u001b[38;5;66;03m# Build and load the HF model — this is the expensive step\u001b[39;00m\n\u001b[32m     10\u001b[39m llm = HFChatModel(\n",
+      "\u001b[31mRuntimeError\u001b[39m: Server not reachable at http://localhost:8000: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /health (Caused by NewConnectionError(\"HTTPConnection(host='localhost', port=8000): Failed to establish a new connection: [Errno 111] Connection refused\"))"
+     ]
+    }
+   ],
    "source": [
     "# Health check first — fail fast before waiting for the large model to load\n",
     "try:\n",
@@ -855,13 +953,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.0"
   }
  },
  "nbformat": 4,

examples/train_rl_agent.py

@@ -0,0 +1,984 @@
+"""Train a deep RL baseline directly against the local Pyre environment.
+
+This script makes the environment contract explicit:
+  - Observation: encoded from `PyreObservation.map_state` into a fixed-length vector
+  - Action: fixed discrete action table with a runtime validity mask from `available_actions_hint`
+  - Reward: the environment's composite reward returned by `PyreEnvironment.step()`
+
+It uses a self-contained NumPy actor-critic implementation so it can run in
+this repository without external ML dependencies.
+
+Examples:
+    python examples/train_rl_agent.py --episodes 150 --difficulty easy
+    python examples/train_rl_agent.py --episodes 300 --difficulty-schedule easy,medium
+    python examples/train_rl_agent.py --episodes 200 --difficulty easy,medium,hard --observation-mode full
+    python examples/train_rl_agent.py --describe-only
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import re
+from collections import deque
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, Iterable, List, Sequence
+
+import numpy as np
+
+from pyre_env.models import PyreAction, PyreObservation
+from pyre_env.server.pyre_env_environment import PyreEnvironment
+
+
+MAX_GRID_W = 24
+MAX_GRID_H = 24
+MAX_DOORS = 16
+DIRECTIONS = ("north", "south", "west", "east")
+WINDS = ("CALM", "NORTH", "SOUTH", "WEST", "EAST")
+DIFFICULTIES = ("easy", "medium", "hard")
+
+MOVE_KEYS = [f"move(direction='{d}')" for d in DIRECTIONS]
+LOOK_KEYS = [f"look(direction='{d}')" for d in DIRECTIONS]
+WAIT_KEY = "wait()"
+OPEN_KEYS = [f"door(target_id='door_{i}', door_state='open')" for i in range(1, MAX_DOORS + 1)]
+CLOSE_KEYS = [f"door(target_id='door_{i}', door_state='close')" for i in range(1, MAX_DOORS + 1)]
+ACTION_KEYS = MOVE_KEYS + LOOK_KEYS + [WAIT_KEY] + OPEN_KEYS + CLOSE_KEYS
+ACTION_DIM = len(ACTION_KEYS)
+ACTION_TO_INDEX = {key: idx for idx, key in enumerate(ACTION_KEYS)}
+
+_MOVE_RE = re.compile(r"move\(direction='(north|south|west|east)'\)")
+_LOOK_RE = re.compile(r"look\(direction='(north|south|west|east)'\)")
+_DOOR_RE = re.compile(r"door\(target_id='(door_(\d+))', door_state='(open|close)'\)")
+
+
+def _one_hot(index: int, size: int) -> np.ndarray:
+    arr = np.zeros(size, dtype=np.float32)
+    if 0 <= index < size:
+        arr[index] = 1.0
+    return arr
+
+
+def action_index_to_env_action(index: int) -> PyreAction:
+    if 0 <= index < 4:
+        return PyreAction(action="move", direction=DIRECTIONS[index])
+    if 4 <= index < 8:
+        return PyreAction(action="look", direction=DIRECTIONS[index - 4])
+    if index == 8:
+        return PyreAction(action="wait")
+    if 9 <= index < 9 + MAX_DOORS:
+        door_id = f"door_{index - 8}"
+        return PyreAction(action="door", target_id=door_id, door_state="open")
+    door_slot = index - (9 + MAX_DOORS)
+    door_id = f"door_{door_slot + 1}"
+    return PyreAction(action="door", target_id=door_id, door_state="close")
+
+
+def build_action_mask(observation: PyreObservation) -> np.ndarray:
+    mask = np.zeros(ACTION_DIM, dtype=np.float32)
+    for hint in observation.available_actions_hint:
+        idx = ACTION_TO_INDEX.get(hint)
+        if idx is not None:
+            mask[idx] = 1.0
+            continue
+
+        match = _MOVE_RE.fullmatch(hint)
+        if match:
+            mask[ACTION_TO_INDEX[f"move(direction='{match.group(1)}')"]] = 1.0
+            continue
+
+        match = _LOOK_RE.fullmatch(hint)
+        if match:
+            mask[ACTION_TO_INDEX[f"look(direction='{match.group(1)}')"]] = 1.0
+            continue
+
+        match = _DOOR_RE.fullmatch(hint)
+        if match:
+            door_id = match.group(1)
+            door_num = int(match.group(2))
+            state = match.group(3)
+            if 1 <= door_num <= MAX_DOORS:
+                mask[ACTION_TO_INDEX[f"door(target_id='{door_id}', door_state='{state}')"]] = 1.0
+
+    if mask.sum() == 0:
+        mask[ACTION_TO_INDEX[WAIT_KEY]] = 1.0
+    return mask
+
+
+class ObservationEncoder:
+    """Encode Pyre observations into a fixed-size float vector."""
+
+    def __init__(self, mode: str = "visible"):
+        if mode not in {"visible", "full"}:
+            raise ValueError(f"Unsupported observation mode: {mode}")
+        self.mode = mode
+        self.base_dim = MAX_GRID_W * MAX_GRID_H * 10 + 22
+
+    def encode(self, observation: PyreObservation) -> np.ndarray:
+        map_state = observation.map_state
+        if map_state is None:
+            raise ValueError("PyreObservation.map_state is required for RL training.")
+
+        cell_one_hot = np.zeros((MAX_GRID_H, MAX_GRID_W, 6), dtype=np.float32)
+        fire_channel = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+        smoke_channel = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+        visible_channel = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+        agent_channel = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+
+        visible = {(x, y) for x, y in map_state.visible_cells}
+        for y in range(map_state.grid_h):
+            for x in range(map_state.grid_w):
+                if self.mode == "visible" and (x, y) not in visible and (x, y) != (map_state.agent_x, map_state.agent_y):
+                    continue
+                i = y * map_state.grid_w + x
+                cell_type = int(map_state.cell_grid[i])
+                if 0 <= cell_type <= 5:
+                    cell_one_hot[y, x, cell_type] = 1.0
+                fire_channel[y, x] = float(map_state.fire_grid[i])
+                smoke_channel[y, x] = float(map_state.smoke_grid[i])
+                visible_channel[y, x] = 1.0 if (x, y) in visible else 0.0
+
+        if 0 <= map_state.agent_x < MAX_GRID_W and 0 <= map_state.agent_y < MAX_GRID_H:
+            agent_channel[map_state.agent_y, map_state.agent_x] = 1.0
+
+        grid_features = np.concatenate(
+            [
+                cell_one_hot.reshape(-1),
+                fire_channel.reshape(-1),
+                smoke_channel.reshape(-1),
+                visible_channel.reshape(-1),
+                agent_channel.reshape(-1),
+            ]
+        )
+
+        metadata = observation.metadata or {}
+        wind_dir = str(metadata.get("wind_dir", map_state.wind_dir or "CALM")).upper()
+        difficulty = str(metadata.get("difficulty", "medium")).lower()
+        wind_index = WINDS.index(wind_dir) if wind_dir in WINDS else 0
+        difficulty_index = DIFFICULTIES.index(difficulty) if difficulty in DIFFICULTIES else 1
+
+        global_features = np.concatenate(
+            [
+                np.array(
+                    [
+                        float(observation.agent_health) / 100.0,
+                        float(map_state.agent_health) / 100.0,
+                        float(map_state.step_count) / max(1, map_state.max_steps),
+                        float(map_state.fire_spread_rate),
+                        float(map_state.humidity),
+                        float(map_state.agent_x) / max(1, map_state.grid_w - 1),
+                        float(map_state.agent_y) / max(1, map_state.grid_h - 1),
+                        float(metadata.get("nearest_exit_distance", MAX_GRID_W + MAX_GRID_H) or 0.0) / float(MAX_GRID_W + MAX_GRID_H),
+                        float(metadata.get("reachable_exit_count", 0.0)) / 4.0,
+                        float(metadata.get("visible_cell_count", 0.0)) / float(MAX_GRID_W * MAX_GRID_H),
+                        float(metadata.get("fire_sources", 0.0)) / 5.0,
+                        {"none": 0.0, "light": 0.33, "moderate": 0.66, "heavy": 1.0}.get(observation.smoke_level, 0.0),
+                        1.0 if map_state.agent_alive else 0.0,
+                        1.0 if map_state.agent_evacuated else 0.0,
+                    ],
+                    dtype=np.float32,
+                ),
+                _one_hot(wind_index, len(WINDS)),
+                _one_hot(difficulty_index, len(DIFFICULTIES)),
+            ]
+        )
+
+        return np.concatenate([grid_features, global_features]).astype(np.float32)
+
+    def describe(self, history_length: int) -> str:
+        grid_text = (
+            f"Observation mode `{self.mode}` encodes a {MAX_GRID_W}x{MAX_GRID_H} padded map with "
+            "10 channels per cell: 6-way cell type one-hot, fire intensity, smoke intensity, visible mask, and agent mask."
+        )
+        if self.mode == "visible":
+            visibility_text = "Only currently visible cells are populated; unseen cells stay zeroed."
+        else:
+            visibility_text = "The full ground-truth map is exposed for curriculum/debug use."
+        return (
+            f"{grid_text} {visibility_text} "
+            f"Global features add health, step progress, fire parameters, position, exit-distance metadata, smoke severity, wind, and difficulty. "
+            f"{history_length} encoded frames are stacked, so the network input dimension is {self.base_dim * history_length}."
+        )
+
+
+def softmax_with_mask(logits: np.ndarray, mask: np.ndarray) -> np.ndarray:
+    masked_logits = np.where(mask > 0.0, logits, -1e9)
+    max_logits = np.max(masked_logits, axis=1, keepdims=True)
+    exps = np.exp(masked_logits - max_logits) * mask
+    denom = np.sum(exps, axis=1, keepdims=True)
+    denom = np.where(denom <= 0.0, 1.0, denom)
+    return exps / denom
+
+
+class AdamOptimizer:
+    def __init__(self, params: Dict[str, np.ndarray], lr: float = 3e-4, beta1: float = 0.9, beta2: float = 0.999):
+        self.lr = lr
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.eps = 1e-8
+        self.t = 0
+        self.m = {k: np.zeros_like(v) for k, v in params.items()}
+        self.v = {k: np.zeros_like(v) for k, v in params.items()}
+
+    def step(self, params: Dict[str, np.ndarray], grads: Dict[str, np.ndarray], clip_norm: float = 1.0) -> None:
+        total_norm_sq = 0.0
+        for grad in grads.values():
+            total_norm_sq += float(np.sum(grad * grad))
+        total_norm = math.sqrt(total_norm_sq)
+        scale = 1.0
+        if total_norm > clip_norm:
+            scale = clip_norm / (total_norm + 1e-8)
+
+        self.t += 1
+        for name, param in params.items():
+            grad = grads[name] * scale
+            self.m[name] = self.beta1 * self.m[name] + (1.0 - self.beta1) * grad
+            self.v[name] = self.beta2 * self.v[name] + (1.0 - self.beta2) * (grad * grad)
+            m_hat = self.m[name] / (1.0 - self.beta1 ** self.t)
+            v_hat = self.v[name] / (1.0 - self.beta2 ** self.t)
+            params[name] -= self.lr * m_hat / (np.sqrt(v_hat) + self.eps)
+
+
+class PolicyValueNetwork:
+    def __init__(self, input_dim: int, action_dim: int, rng: np.random.Generator, hidden_sizes: Sequence[int] = (256, 128)):
+        h1, h2 = hidden_sizes
+        self.params: Dict[str, np.ndarray] = {
+            "w1": self._init_weight(rng, input_dim, h1),
+            "b1": np.zeros(h1, dtype=np.float32),
+            "w2": self._init_weight(rng, h1, h2),
+            "b2": np.zeros(h2, dtype=np.float32),
+            "wp": self._init_weight(rng, h2, action_dim),
+            "bp": np.zeros(action_dim, dtype=np.float32),
+            "wv": self._init_weight(rng, h2, 1),
+            "bv": np.zeros(1, dtype=np.float32),
+        }
+        self.optimizer = AdamOptimizer(self.params)
+
+    @staticmethod
+    def _init_weight(rng: np.random.Generator, in_dim: int, out_dim: int) -> np.ndarray:
+        scale = math.sqrt(2.0 / max(1, in_dim + out_dim))
+        return (rng.standard_normal((in_dim, out_dim)) * scale).astype(np.float32)
+
+    def forward(self, x: np.ndarray) -> tuple[np.ndarray, np.ndarray, Dict[str, np.ndarray]]:
+        z1 = x @ self.params["w1"] + self.params["b1"]
+        h1 = np.tanh(z1)
+        z2 = h1 @ self.params["w2"] + self.params["b2"]
+        h2 = np.tanh(z2)
+        logits = h2 @ self.params["wp"] + self.params["bp"]
+        values = (h2 @ self.params["wv"] + self.params["bv"]).reshape(-1)
+        cache = {"x": x, "h1": h1, "h2": h2}
+        return logits, values, cache
+
+    def predict(self, x: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, float]:
+        logits, values, _ = self.forward(x[None, :])
+        probs = softmax_with_mask(logits, mask[None, :])[0]
+        return probs, float(values[0])
+
+    def update(
+        self,
+        states: np.ndarray,
+        masks: np.ndarray,
+        actions: np.ndarray,
+        returns: np.ndarray,
+        advantages: np.ndarray,
+        value_coef: float = 0.5,
+    ) -> Dict[str, float]:
+        logits, values, cache = self.forward(states)
+        probs = softmax_with_mask(logits, masks)
+
+        batch_size = max(1, states.shape[0])
+        grad_logits = probs.copy()
+        grad_logits[np.arange(batch_size), actions] -= 1.0
+        grad_logits *= advantages[:, None] / batch_size
+        grad_logits *= masks
+
+        grad_values = ((values - returns)[:, None] * value_coef) / batch_size
+
+        grads: Dict[str, np.ndarray] = {}
+        grads["wp"] = cache["h2"].T @ grad_logits
+        grads["bp"] = np.sum(grad_logits, axis=0)
+        grads["wv"] = cache["h2"].T @ grad_values
+        grads["bv"] = np.sum(grad_values, axis=0)
+
+        dh2 = grad_logits @ self.params["wp"].T + grad_values @ self.params["wv"].T
+        dz2 = dh2 * (1.0 - cache["h2"] ** 2)
+        grads["w2"] = cache["h1"].T @ dz2
+        grads["b2"] = np.sum(dz2, axis=0)
+
+        dh1 = dz2 @ self.params["w2"].T
+        dz1 = dh1 * (1.0 - cache["h1"] ** 2)
+        grads["w1"] = cache["x"].T @ dz1
+        grads["b1"] = np.sum(dz1, axis=0)
+
+        self.optimizer.step(self.params, grads, clip_norm=1.0)
+
+        chosen_probs = np.clip(probs[np.arange(batch_size), actions], 1e-8, 1.0)
+        policy_loss = float(-np.mean(advantages * np.log(chosen_probs)))
+        value_loss = float(0.5 * np.mean((values - returns) ** 2))
+        entropy = float(-np.mean(np.sum(np.where(probs > 0.0, probs * np.log(np.clip(probs, 1e-8, 1.0)), 0.0), axis=1)))
+        return {
+            "policy_loss": policy_loss,
+            "value_loss": value_loss,
+            "entropy": entropy,
+            "mean_value": float(np.mean(values)),
+        }
+
+    def save(self, path: Path, metadata: Dict[str, object]) -> None:
+        path.parent.mkdir(parents=True, exist_ok=True)
+        arrays = {name: value for name, value in self.params.items()}
+        arrays["metadata_json"] = np.array(json.dumps(metadata))
+        np.savez(path, **arrays)
+
+
+@dataclass
+class Trajectory:
+    states: List[np.ndarray]
+    masks: List[np.ndarray]
+    actions: List[int]
+    rewards: List[float]
+    values: List[float]
+    evacuated: bool
+    final_health: float
+    steps: int
+    total_reward: float
+
+
+def compute_gae(
+    rewards: Sequence[float],
+    values: Sequence[float],
+    gamma: float,
+    gae_lambda: float,
+) -> tuple[np.ndarray, np.ndarray]:
+    rewards_arr = np.asarray(rewards, dtype=np.float32)
+    values_arr = np.asarray(values, dtype=np.float32)
+    advantages = np.zeros(len(rewards_arr), dtype=np.float32)
+    gae = 0.0
+    next_value = 0.0
+    for i in range(len(rewards_arr) - 1, -1, -1):
+        delta = rewards_arr[i] + gamma * next_value - values_arr[i]
+        gae = delta + gamma * gae_lambda * gae
+        advantages[i] = gae
+        next_value = values_arr[i]
+    returns = advantages + values_arr
+    return returns.astype(np.float32), advantages.astype(np.float32)
+
+
+def select_action(
+    network: PolicyValueNetwork,
+    state_vec: np.ndarray,
+    mask: np.ndarray,
+    rng: np.random.Generator,
+    greedy: bool = False,
+) -> tuple[int, float]:
+    probs, value = network.predict(state_vec, mask)
+    valid_indices = np.flatnonzero(mask > 0.0)
+    if len(valid_indices) == 0:
+        return ACTION_TO_INDEX[WAIT_KEY], value
+    if greedy:
+        best_local = int(np.argmax(probs[valid_indices]))
+        return int(valid_indices[best_local]), value
+    return int(rng.choice(np.arange(len(probs)), p=probs)), value
+
+
+def build_stacked_state(frames: deque[np.ndarray]) -> np.ndarray:
+    return np.concatenate(list(frames), dtype=np.float32)
+
+
+def run_episode(
+    env: PyreEnvironment,
+    network: PolicyValueNetwork,
+    encoder: ObservationEncoder,
+    rng: np.random.Generator,
+    difficulty: str,
+    history_length: int,
+    greedy: bool = False,
+) -> Trajectory:
+    observation = env.reset(difficulty=difficulty)
+    zero_frame = np.zeros(encoder.base_dim, dtype=np.float32)
+    frames: deque[np.ndarray] = deque([zero_frame.copy() for _ in range(history_length)], maxlen=history_length)
+    frames.append(encoder.encode(observation))
+
+    states: List[np.ndarray] = []
+    masks: List[np.ndarray] = []
+    actions: List[int] = []
+    rewards: List[float] = []
+    values: List[float] = []
+
+    total_reward = 0.0
+    final_health = observation.agent_health
+    evacuated = False
+    steps = 0
+
+    while True:
+        state_vec = build_stacked_state(frames)
+        mask = build_action_mask(observation)
+        action_idx, value = select_action(network, state_vec, mask, rng, greedy=greedy)
+        action = action_index_to_env_action(action_idx)
+
+        next_obs = env.step(action)
+        reward = float(next_obs.reward or 0.0)
+
+        states.append(state_vec)
+        masks.append(mask)
+        actions.append(action_idx)
+        rewards.append(reward)
+        values.append(value)
+
+        total_reward += reward
+        steps += 1
+        final_health = next_obs.agent_health
+        evacuated = next_obs.agent_evacuated
+
+        frames.append(encoder.encode(next_obs))
+        observation = next_obs
+        if next_obs.done:
+            break
+
+    return Trajectory(
+        states=states,
+        masks=masks,
+        actions=actions,
+        rewards=rewards,
+        values=values,
+        evacuated=evacuated,
+        final_health=final_health,
+        steps=steps,
+        total_reward=total_reward,
+    )
+
+
+def evaluate_policy(
+    env: PyreEnvironment,
+    network: PolicyValueNetwork,
+    encoder: ObservationEncoder,
+    rng: np.random.Generator,
+    difficulty: str,
+    history_length: int,
+    episodes: int,
+) -> Dict[str, float]:
+    rewards = []
+    evacuations = 0
+    lengths = []
+    for _ in range(episodes):
+        traj = run_episode(env, network, encoder, rng, difficulty, history_length, greedy=True)
+        rewards.append(traj.total_reward)
+        lengths.append(traj.steps)
+        evacuations += int(traj.evacuated)
+    return {
+        "eval_reward_mean": float(np.mean(rewards)) if rewards else 0.0,
+        "eval_reward_max": float(np.max(rewards)) if rewards else 0.0,
+        "eval_success_rate": float(evacuations / max(1, episodes)),
+        "eval_steps_mean": float(np.mean(lengths)) if lengths else 0.0,
+    }
+
+
+def expand_difficulty_schedule(schedule_text: str, episodes: int) -> List[str]:
+    stages = [part.strip().lower() for part in schedule_text.split(",") if part.strip()]
+    if not stages:
+        stages = ["medium"]
+    for stage in stages:
+        if stage not in DIFFICULTIES:
+            raise ValueError(f"Invalid difficulty in schedule: {stage}")
+    segment = max(1, episodes // len(stages))
+    expanded: List[str] = []
+    for stage in stages:
+        expanded.extend([stage] * segment)
+    while len(expanded) < episodes:
+        expanded.append(stages[-1])
+    return expanded[:episodes]
+
+
+def describe_environment_contract(encoder: ObservationEncoder, history_length: int) -> str:
+    action_text = (
+        f"Action space has {ACTION_DIM} fixed discrete actions: 4 moves, 4 looks, wait, "
+        f"{MAX_DOORS} door-open slots, and {MAX_DOORS} door-close slots. "
+        "A per-step mask from `available_actions_hint` prevents invalid actions."
+    )
+    reward_text = (
+        "Reward comes directly from the environment's composite rubric: time penalty, exit progress, "
+        "progress regression penalty, safe-progress bonus, danger penalty, health-drain penalty, "
+        "strategic door bonus, exploration bonus, plus terminal evacuation/death/timeout/near-miss/time bonuses."
+    )
+    return "\n".join(
+        [
+            "Pyre RL contract",
+            encoder.describe(history_length),
+            action_text,
+            reward_text,
+        ]
+    )
+
+
+def _moving_average(values: Sequence[float], window: int) -> List[float]:
+    if not values:
+        return []
+    out: List[float] = []
+    run = 0.0
+    q: deque[float] = deque()
+    for value in values:
+        q.append(float(value))
+        run += float(value)
+        if len(q) > window:
+            run -= q.popleft()
+        out.append(run / len(q))
+    return out
+
+
+def save_metrics_csv(path: Path, rows: List[Dict[str, float | int | str]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    if not rows:
+        return
+    with path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def save_training_graph(path: Path, episode_rows: List[Dict[str, float | int | str]], eval_rows: List[Dict[str, float | int | str]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    if not episode_rows:
+        return
+
+    width = 1260
+    height = 780
+    margin_left = 100   # extra room for rotated Y-axis label + tick values
+    margin_right = 110  # extra room for right axis label + tick values
+    margin_top = 70     # room for title
+    margin_bottom = 90  # room for X-axis label + tick values + legend
+    plot_w = width - margin_left - margin_right
+    plot_h = height - margin_top - margin_bottom
+
+    # X: plot_left=100, plot_right=1150  Y: plot_top=70, plot_bottom=690
+
+    episodes = [int(r["episode"]) for r in episode_rows]
+    rewards = [float(r["reward"]) for r in episode_rows]
+    reward_ma = _moving_average(rewards, 20)
+    success_ma = _moving_average([float(r["evacuated"]) for r in episode_rows], 20)
+
+    all_reward_values = rewards + reward_ma + [float(r["reward_mean"]) for r in eval_rows] + [float(r["reward_max"]) for r in eval_rows]
+    y_min = min(all_reward_values) if all_reward_values else -1.0
+    y_max = max(all_reward_values) if all_reward_values else 1.0
+    if abs(y_max - y_min) < 1e-6:
+        y_min -= 1.0
+        y_max += 1.0
+    y_pad = 0.1 * (y_max - y_min)
+    y_min -= y_pad
+    y_max += y_pad
+
+    max_episode = max(episodes) if episodes else 1
+
+    plot_left = margin_left
+    plot_right = margin_left + plot_w
+    plot_top = margin_top
+    plot_bottom = margin_top + plot_h
+
+    def x_pos(ep: float) -> float:
+        return plot_left + (float(ep) - 1.0) / max(1.0, max_episode - 1.0) * plot_w
+
+    def y_pos_reward(value: float) -> float:
+        return plot_top + (y_max - float(value)) / max(1e-6, (y_max - y_min)) * plot_h
+
+    def y_pos_success(value: float) -> float:
+        return plot_top + (1.0 - float(value)) * plot_h
+
+    def polyline(points: List[tuple[float, float]]) -> str:
+        return " ".join(f"{x:.1f},{y:.1f}" for x, y in points)
+
+    reward_points    = [(x_pos(ep), y_pos_reward(val)) for ep, val in zip(episodes, rewards)]
+    reward_ma_points = [(x_pos(ep), y_pos_reward(val)) for ep, val in zip(episodes, reward_ma)]
+    success_points   = [(x_pos(ep), y_pos_success(val)) for ep, val in zip(episodes, success_ma)]
+    eval_points      = [(x_pos(float(r["episode"])), y_pos_success(float(r["success_rate"]))) for r in eval_rows]
+
+    n_x_ticks = 8
+    episode_ticks = sorted(set(
+        max(1, round(1 + i * (max_episode - 1) / n_x_ticks))
+        for i in range(n_x_ticks + 1)
+    ))
+    n_y_ticks = 6
+    reward_ticks  = [y_min + (y_max - y_min) * i / n_y_ticks for i in range(n_y_ticks + 1)]
+    success_ticks = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
+
+    svg = []
+    svg.append(f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">')
+
+    # Background
+    svg.append('<rect width="100%" height="100%" fill="#f7f5ef"/>')
+
+    # Title + subtitle
+    svg.append(f'<text x="{plot_left}" y="28" font-family="Georgia, serif" font-size="22" font-weight="bold" fill="#1d2a38">Pyre RL Training</text>')
+    svg.append(f'<text x="{plot_left}" y="50" font-family="Georgia, serif" font-size="13" fill="#5b6770">Left axis: Reward   |   Right axis: Success Rate (0–1)</text>')
+
+    # Plot area background + border
+    svg.append(f'<rect x="{plot_left}" y="{plot_top}" width="{plot_w}" height="{plot_h}" fill="#fffdf8" stroke="#b8b0a2" stroke-width="1.5"/>')
+
+    # ── Vertical grid lines + X-axis ticks ──────────────────────────────────
+    for tick in episode_ticks:
+        x = x_pos(float(tick))
+        # dashed grid line
+        svg.append(f'<line x1="{x:.1f}" y1="{plot_top}" x2="{x:.1f}" y2="{plot_bottom}" '
+                   f'stroke="#d8d2c8" stroke-width="1" stroke-dasharray="4,4"/>')
+        # solid tick mark on bottom axis
+        svg.append(f'<line x1="{x:.1f}" y1="{plot_bottom}" x2="{x:.1f}" y2="{plot_bottom + 6}" '
+                   f'stroke="#6b6460" stroke-width="1.5"/>')
+        # tick label
+        svg.append(f'<text x="{x:.1f}" y="{plot_bottom + 20}" text-anchor="middle" '
+                   f'font-family="Georgia, serif" font-size="12" fill="#4a4540">{tick}</text>')
+
+    # X-axis title
+    x_title_x = plot_left + plot_w / 2
+    x_title_y = plot_bottom + 50
+    svg.append(f'<text x="{x_title_x:.1f}" y="{x_title_y}" text-anchor="middle" '
+               f'font-family="Georgia, serif" font-size="14" font-weight="bold" fill="#1d2a38">Episode</text>')
+
+    # ── Horizontal grid lines + Left Y-axis ticks (Reward) ──────────────────
+    for tick in reward_ticks:
+        y = y_pos_reward(tick)
+        # dashed grid line
+        svg.append(f'<line x1="{plot_left}" y1="{y:.1f}" x2="{plot_right}" y2="{y:.1f}" '
+                   f'stroke="#d8d2c8" stroke-width="1" stroke-dasharray="4,4"/>')
+        # solid tick mark on left axis
+        svg.append(f'<line x1="{plot_left - 6}" y1="{y:.1f}" x2="{plot_left}" y2="{y:.1f}" '
+                   f'stroke="#6b6460" stroke-width="1.5"/>')
+        # tick label
+        svg.append(f'<text x="{plot_left - 10}" y="{y + 4:.1f}" text-anchor="end" '
+                   f'font-family="Georgia, serif" font-size="12" fill="#8a4b08">{tick:.1f}</text>')
+
+    # Left Y-axis title (rotated) — centered on plot height
+    ly_cx = plot_left - 70
+    ly_cy = plot_top + plot_h / 2
+    svg.append(f'<text transform="rotate(-90, {ly_cx:.1f}, {ly_cy:.1f})" '
+               f'x="{ly_cx:.1f}" y="{ly_cy:.1f}" text-anchor="middle" '
+               f'font-family="Georgia, serif" font-size="14" font-weight="bold" fill="#8a4b08">Reward</text>')
+
+    # ── Right Y-axis ticks (Success Rate) ───────────────────────────────────
+    for tick in success_ticks:
+        y = y_pos_success(tick)
+        # solid tick mark on right axis
+        svg.append(f'<line x1="{plot_right}" y1="{y:.1f}" x2="{plot_right + 6}" y2="{y:.1f}" '
+                   f'stroke="#6b6460" stroke-width="1.5"/>')
+        # tick label
+        svg.append(f'<text x="{plot_right + 12}" y="{y + 4:.1f}" '
+                   f'font-family="Georgia, serif" font-size="12" fill="#0d5b6b">{tick:.2f}</text>')
+
+    # Right Y-axis title (rotated)
+    ry_cx = plot_right + 85
+    ry_cy = plot_top + plot_h / 2
+    svg.append(f'<text transform="rotate(90, {ry_cx:.1f}, {ry_cy:.1f})" '
+               f'x="{ry_cx:.1f}" y="{ry_cy:.1f}" text-anchor="middle" '
+               f'font-family="Georgia, serif" font-size="14" font-weight="bold" fill="#0d5b6b">Success Rate</text>')
+
+    # ── Axis border lines (solid, on top of grid) ────────────────────────────
+    # Bottom axis
+    svg.append(f'<line x1="{plot_left}" y1="{plot_bottom}" x2="{plot_right}" y2="{plot_bottom}" '
+               f'stroke="#6b6460" stroke-width="2"/>')
+    # Left axis
+    svg.append(f'<line x1="{plot_left}" y1="{plot_top}" x2="{plot_left}" y2="{plot_bottom}" '
+               f'stroke="#6b6460" stroke-width="2"/>')
+    # Right axis
+    svg.append(f'<line x1="{plot_right}" y1="{plot_top}" x2="{plot_right}" y2="{plot_bottom}" '
+               f'stroke="#6b6460" stroke-width="2"/>')
+
+    # ── Data series ─────────────────────────────────────────────────────────
+    # Raw episode reward (faint)
+    svg.append(f'<polyline fill="none" stroke="#c5bfb1" stroke-width="1.5" points="{polyline(reward_points)}"/>')
+    # Reward moving average
+    svg.append(f'<polyline fill="none" stroke="#c1661c" stroke-width="3" stroke-linejoin="round" points="{polyline(reward_ma_points)}"/>')
+    # Success moving average
+    svg.append(f'<polyline fill="none" stroke="#127a8a" stroke-width="3" stroke-linejoin="round" points="{polyline(success_points)}"/>')
+    # Eval checkpoints
+    for x, y in eval_points:
+        svg.append(f'<circle cx="{x:.1f}" cy="{y:.1f}" r="5" fill="#0d5b6b" stroke="#ffffff" stroke-width="2"/>')
+
+    # ── Legend ───────────────────────────────────────────────────────────────
+    legend_y = plot_bottom + 72
+    items = [
+        ("#c1661c", 3,   False, "Reward (moving avg)"),
+        ("#127a8a", 3,   False, "Success rate (moving avg)"),
+        ("#c5bfb1", 1.5, False, "Episode reward"),
+        ("#0d5b6b", 0,   True,  "Eval success checkpoint"),
+    ]
+    lx = plot_left
+    for color, sw, is_dot, label in items:
+        if is_dot:
+            svg.append(f'<circle cx="{lx + 15}" cy="{legend_y - 4}" r="5" fill="{color}" stroke="#ffffff" stroke-width="2"/>')
+        else:
+            svg.append(f'<line x1="{lx}" y1="{legend_y - 4}" x2="{lx + 30}" y2="{legend_y - 4}" stroke="{color}" stroke-width="{sw}"/>')
+        svg.append(f'<text x="{lx + 36}" y="{legend_y}" font-family="Georgia, serif" font-size="12" fill="#1d2a38">{label}</text>')
+        lx += 230
+
+    svg.append("</svg>")
+    path.write_text("\n".join(svg), encoding="utf-8")
+
+
+def train(args: argparse.Namespace) -> None:
+    rng = np.random.default_rng(args.seed)
+    encoder = ObservationEncoder(mode=args.observation_mode)
+    difficulty_schedule = expand_difficulty_schedule(args.difficulty_schedule, args.episodes)
+    input_dim = encoder.base_dim * args.history_length
+    network = PolicyValueNetwork(input_dim=input_dim, action_dim=ACTION_DIM, rng=rng)
+    env = PyreEnvironment(max_steps=args.max_steps)
+
+    print(describe_environment_contract(encoder, args.history_length))
+    print("")
+
+    batch_states: List[np.ndarray] = []
+    batch_masks: List[np.ndarray] = []
+    batch_actions: List[int] = []
+    batch_returns: List[np.ndarray] = []
+    batch_advantages: List[np.ndarray] = []
+
+    reward_window: deque[float] = deque(maxlen=20)
+    success_window: deque[float] = deque(maxlen=20)
+    episode_metrics: List[Dict[str, float | int | str]] = []
+    eval_metrics_rows: List[Dict[str, float | int | str]] = []
+
+    for episode_idx in range(args.episodes):
+        difficulty = difficulty_schedule[episode_idx] if args.difficulty_schedule else args.difficulty
+        traj = run_episode(
+            env=env,
+            network=network,
+            encoder=encoder,
+            rng=rng,
+            difficulty=difficulty,
+            history_length=args.history_length,
+            greedy=False,
+        )
+
+        returns, advantages = compute_gae(traj.rewards, traj.values, args.gamma, args.gae_lambda)
+        batch_states.extend(traj.states)
+        batch_masks.extend(traj.masks)
+        batch_actions.extend(traj.actions)
+        batch_returns.append(returns)
+        batch_advantages.append(advantages)
+
+        reward_window.append(traj.total_reward)
+        success_window.append(float(traj.evacuated))
+        episode_metrics.append(
+            {
+                "episode": episode_idx + 1,
+                "difficulty": difficulty,
+                "reward": round(traj.total_reward, 4),
+                "evacuated": int(traj.evacuated),
+                "steps": traj.steps,
+                "final_health": round(traj.final_health, 2),
+                "reward_mean_20": round(float(np.mean(reward_window)), 4),
+                "success_rate_20": round(float(np.mean(success_window)), 4),
+            }
+        )
+
+        print(
+            f"episode={episode_idx + 1:04d} difficulty={difficulty:<6} "
+            f"steps={traj.steps:03d} reward={traj.total_reward:+8.3f} "
+            f"evacuated={int(traj.evacuated)} health={traj.final_health:6.1f}"
+        )
+
+        should_update = (episode_idx + 1) % args.update_every == 0 or (episode_idx + 1) == args.episodes
+        if should_update and batch_states:
+            states_arr = np.asarray(batch_states, dtype=np.float32)
+            masks_arr = np.asarray(batch_masks, dtype=np.float32)
+            actions_arr = np.asarray(batch_actions, dtype=np.int64)
+            returns_arr = np.concatenate(batch_returns).astype(np.float32)
+            advantages_arr = np.concatenate(batch_advantages).astype(np.float32)
+            advantages_arr = (advantages_arr - advantages_arr.mean()) / (advantages_arr.std() + 1e-8)
+
+            network.optimizer.lr = args.learning_rate
+            metrics = {}
+            for _ in range(args.update_epochs):
+                order = rng.permutation(len(states_arr))
+                for start in range(0, len(states_arr), args.minibatch_size):
+                    idx = order[start:start + args.minibatch_size]
+                    metrics = network.update(
+                        states=states_arr[idx],
+                        masks=masks_arr[idx],
+                        actions=actions_arr[idx],
+                        returns=returns_arr[idx],
+                        advantages=advantages_arr[idx],
+                        value_coef=args.value_coef,
+                    )
+
+            print(
+                f"update  episodes={episode_idx + 1:04d} samples={len(states_arr):05d} "
+                f"reward_mean20={np.mean(reward_window):+8.3f} success20={np.mean(success_window):.2f} "
+                f"policy_loss={metrics['policy_loss']:+.4f} value_loss={metrics['value_loss']:.4f} "
+                f"entropy={metrics['entropy']:.4f}"
+            )
+
+            batch_states.clear()
+            batch_masks.clear()
+            batch_actions.clear()
+            batch_returns.clear()
+            batch_advantages.clear()
+
+        should_eval = args.eval_every > 0 and ((episode_idx + 1) % args.eval_every == 0 or (episode_idx + 1) == args.episodes)
+        if should_eval:
+            eval_metrics = evaluate_policy(
+                env=env,
+                network=network,
+                encoder=encoder,
+                rng=rng,
+                difficulty=args.eval_difficulty,
+                history_length=args.history_length,
+                episodes=args.eval_episodes,
+            )
+            print(
+                f"eval    episodes={episode_idx + 1:04d} difficulty={args.eval_difficulty:<6} "
+                f"reward_mean={eval_metrics['eval_reward_mean']:+8.3f} "
+                f"reward_max={eval_metrics['eval_reward_max']:+8.3f} "
+                f"success={eval_metrics['eval_success_rate']:.2f} "
+                f"steps={eval_metrics['eval_steps_mean']:.1f}"
+            )
+            eval_metrics_rows.append(
+                {
+                    "episode": episode_idx + 1,
+                    "difficulty": args.eval_difficulty,
+                    "reward_mean": round(eval_metrics["eval_reward_mean"], 4),
+                    "reward_max": round(eval_metrics["eval_reward_max"], 4),
+                    "success_rate": round(eval_metrics["eval_success_rate"], 4),
+                    "steps_mean": round(eval_metrics["eval_steps_mean"], 4),
+                }
+            )
+
+    if args.output:
+        output_path = Path(args.output)
+        network.save(
+            output_path,
+            metadata={
+                "observation_mode": args.observation_mode,
+                "history_length": args.history_length,
+                "episodes": args.episodes,
+                "difficulty": args.difficulty,
+                "difficulty_schedule": args.difficulty_schedule,
+                "gamma": args.gamma,
+                "gae_lambda": args.gae_lambda,
+                "learning_rate": args.learning_rate,
+                "update_epochs": args.update_epochs,
+                "minibatch_size": args.minibatch_size,
+                "action_dim": ACTION_DIM,
+                "input_dim": input_dim,
+            },
+        )
+        print(f"saved   model={output_path}")
+        if args.save_metrics:
+            metrics_path = output_path.with_suffix(".csv")
+            save_metrics_csv(metrics_path, episode_metrics)
+            print(f"saved   metrics={metrics_path}")
+        if args.save_graph:
+            graph_path = output_path.with_suffix(".svg")
+            save_training_graph(graph_path, episode_metrics, eval_metrics_rows)
+            print(f"saved   graph={graph_path}")
+            # Also save PNG
+            try:
+                import matplotlib
+                matplotlib.use("Agg")
+                import matplotlib.pyplot as plt
+                import matplotlib.ticker as mticker
+                import matplotlib.patches as mpatches
+
+                episodes_list  = [int(r["episode"])    for r in episode_metrics]
+                rewards_list   = [float(r["reward"])   for r in episode_metrics]
+                evacuated_list = [float(r["evacuated"]) for r in episode_metrics]
+                diff_list      = [str(r["difficulty"]) for r in episode_metrics]
+
+                def _ma(vals, w=20):
+                    out, run, q = [], 0.0, []
+                    for v in vals:
+                        q.append(v); run += v
+                        if len(q) > w: run -= q.pop(0)
+                        out.append(run / len(q))
+                    return out
+
+                reward_ma  = _ma(rewards_list)
+                success_ma = _ma(evacuated_list)
+                eval_eps   = [int(r["episode"])       for r in eval_metrics_rows]
+                eval_succ  = [float(r["success_rate"]) for r in eval_metrics_rows]
+
+                diff_colors = {"easy": "#d4edda", "medium": "#fff3cd", "hard": "#f8d7da"}
+                regions = []
+                if diff_list:
+                    cur, start = diff_list[0], episodes_list[0]
+                    for ep, d in zip(episodes_list[1:], diff_list[1:]):
+                        if d != cur:
+                            regions.append((start, ep, cur)); cur, start = d, ep
+                    regions.append((start, episodes_list[-1], cur))
+
+                fig, ax1 = plt.subplots(figsize=(14, 6))
+                ax2 = ax1.twinx()
+                for x0, x1, diff in regions:
+                    ax1.axvspan(x0, x1, color=diff_colors.get(diff, "#eeeeee"), alpha=0.35, zorder=0)
+                ax1.axhline(0, color="#aaaaaa", linewidth=0.8, linestyle="--", zorder=1)
+                ax1.plot(episodes_list, rewards_list,  color="#d1c7bc", linewidth=0.8, alpha=0.6, label="Episode reward", zorder=2)
+                ax1.plot(episodes_list, reward_ma,     color="#c1661c", linewidth=2.5, label="Reward (MA-20)", zorder=3)
+                ax2.plot(episodes_list, success_ma,    color="#1a7a8a", linewidth=2.5, label="Success rate (MA-20)", zorder=3)
+                if eval_eps:
+                    ax2.scatter(eval_eps, eval_succ, color="#0d5b6b", s=60, zorder=5, marker="D", edgecolors="white", linewidths=1.2, label="Eval success")
+                ax1.set_xlabel("Episode", fontsize=13, fontweight="bold", labelpad=8)
+                ax1.set_ylabel("Reward",  fontsize=13, fontweight="bold", color="#c1661c", labelpad=8)
+                ax2.set_ylabel("Success Rate", fontsize=13, fontweight="bold", color="#1a7a8a", labelpad=8)
+                ax1.tick_params(axis="y", labelcolor="#c1661c")
+                ax2.tick_params(axis="y", labelcolor="#1a7a8a")
+                ax2.set_ylim(-0.05, 1.05)
+                ax2.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1.0, decimals=0))
+                ax1.grid(True, linestyle="--", linewidth=0.6, color="#dddddd", alpha=0.8)
+                ax1.set_xlim(episodes_list[0], episodes_list[-1])
+                diff_patches = [mpatches.Patch(color=diff_colors[d], alpha=0.6, label=d.capitalize())
+                                for d in ["easy", "medium", "hard"] if d in diff_list]
+                h1, l1 = ax1.get_legend_handles_labels()
+                h2, l2 = ax2.get_legend_handles_labels()
+                ax1.legend(h1 + h2 + diff_patches, l1 + l2 + [p.get_label() for p in diff_patches],
+                           loc="upper left", fontsize=9, framealpha=0.85)
+                final_sr = success_ma[-1] if success_ma else 0.0
+                fig.suptitle(f"Pyre NumPy A2C Training  —  {episodes_list[-1]} episodes  |  final success: {final_sr:.0%}",
+                             fontsize=14, fontweight="bold", y=1.01)
+                fig.tight_layout()
+                png_path = output_path.with_suffix(".png")
+                fig.savefig(png_path, dpi=150, bbox_inches="tight")
+                plt.close(fig)
+                print(f"saved   graph_png={png_path}")
+            except ImportError:
+                pass
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Train a NumPy actor-critic baseline for Pyre.")
+    parser.add_argument("--episodes", type=int, default=120, help="Training episodes.")
+    parser.add_argument("--difficulty", type=str, default="easy", choices=DIFFICULTIES)
+    parser.add_argument(
+        "--difficulty-schedule",
+        type=str,
+        default="easy,medium",
+        help="Comma-separated curriculum, expanded evenly across episodes.",
+    )
+    parser.add_argument("--eval-difficulty", type=str, default="medium", choices=DIFFICULTIES)
+    parser.add_argument("--eval-episodes", type=int, default=5)
+    parser.add_argument("--eval-every", type=int, default=20)
+    parser.add_argument("--update-every", type=int, default=5, help="Episodes per policy update.")
+    parser.add_argument("--update-epochs", type=int, default=3, help="Gradient passes over each on-policy batch.")
+    parser.add_argument("--minibatch-size", type=int, default=256, help="Samples per gradient step.")
+    parser.add_argument("--gamma", type=float, default=0.99)
+    parser.add_argument("--gae-lambda", type=float, default=0.95)
+    parser.add_argument("--learning-rate", type=float, default=3e-4)
+    parser.add_argument("--value-coef", type=float, default=0.5)
+    parser.add_argument("--history-length", type=int, default=4)
+    parser.add_argument("--max-steps", type=int, default=150)
+    parser.add_argument("--seed", type=int, default=7)
+    parser.add_argument("--observation-mode", type=str, default="visible", choices=("visible", "full"))
+    parser.add_argument("--output", type=str, default="artifacts/pyre_actor_critic.npz")
+    parser.add_argument("--save-metrics", action="store_true", help="Save per-episode metrics as CSV beside the model.")
+    parser.add_argument("--save-graph", action="store_true", help="Save an SVG training graph beside the model.")
+    parser.add_argument("--describe-only", action="store_true", help="Print observation/action/reward definitions and exit.")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    encoder = ObservationEncoder(mode=args.observation_mode)
+    if args.describe_only:
+        print(describe_environment_contract(encoder, args.history_length))
+        return
+    train(args)
+
+
+if __name__ == "__main__":
+    main()

examples/train_sb3_agent.py

@@ -0,0 +1,285 @@
+import sys
+import os
+import types
+
+# Windows AppControl can block matplotlib's compiled C extensions.
+# Stub the minimal surface that stable_baselines3.common.logger imports
+# at module level so SB3 loads cleanly even without a working matplotlib.
+def _stub_matplotlib():
+    if "matplotlib" in sys.modules:
+        return
+    _mpl = types.ModuleType("matplotlib")
+    _mpl.figure = types.ModuleType("matplotlib.figure")
+    _mpl.figure.Figure = object
+    _mpl.use = lambda *a, **kw: None
+    _mpl.__version__ = "0.0.0"
+    sys.modules["matplotlib"] = _mpl
+    sys.modules["matplotlib.figure"] = _mpl.figure
+    for sub in ("matplotlib.pyplot", "matplotlib.ticker", "matplotlib.patches",
+                "matplotlib.gridspec", "matplotlib.colors", "matplotlib.cm",
+                "matplotlib.backend_bases", "matplotlib.backends",
+                "matplotlib.backends.backend_agg"):
+        m = types.ModuleType(sub)
+        sys.modules[sub] = m
+
+_stub_matplotlib()
+
+import gymnasium as gym
+import numpy as np
+from gymnasium import spaces
+from pyre_env.models import PyreAction, PyreObservation
+from pyre_env.server.pyre_env_environment import PyreEnvironment
+import torch as th
+sys.path.append(os.getcwd())
+
+class PyreGymEnv(gym.Env):
+    """Gymnasium wrapper for PyreEnvironment."""
+    
+    def __init__(self, difficulty="easy", max_steps=150, observation_mode="visible"):
+        super().__init__()
+        self.env = PyreEnvironment(max_steps=max_steps)
+        self.difficulty = difficulty
+        self.observation_mode = observation_mode
+        
+        # Action space: 
+        # 0-3: Move (N, S, W, E)
+        # 4-7: Look (N, S, W, E)
+        # 8: Wait
+        # 9-24: Open Door 1-16
+        # 25-40: Close Door 1-16
+        self.action_space = spaces.Discrete(41)
+        
+        # Observation space: Multi-input
+        # 1. Grid: 24x24x7 (Floor, Wall, Door_Open, Door_Closed, Exit, Obstacle, Fire, Smoke)
+        # 2. Global: [health, oxygen, step_progress, fire_spread, humidity, agent_x, agent_y, nearest_exit_dist, is_coughing]
+        # 3. Heat Sensor: 3x3
+        self.observation_space = spaces.Dict({
+            "grid": spaces.Box(low=0, high=1, shape=(7, 24, 24), dtype=np.float32),
+            "global": spaces.Box(low=0, high=1, shape=(9,), dtype=np.float32),
+            "heat": spaces.Box(low=0, high=1, shape=(1, 3, 3), dtype=np.float32)
+        })
+
+    def _get_obs(self, pyre_obs: PyreObservation):
+        map_state = pyre_obs.map_state
+        w, h = map_state.grid_w, map_state.grid_h
+        
+        # Build 7-channel grid
+        # Channels: 0:Wall, 1:Door_Open, 2:Door_Closed, 3:Exit, 4:Obstacle, 5:Fire, 6:Smoke
+        # (Floor is implicit as all zeros in other channels)
+        grid = np.zeros((7, 24, 24), dtype=np.float32)
+        
+        visible = {(x, y) for x, y in map_state.visible_cells}
+        for y in range(h):
+            for x in range(w):
+                if self.observation_mode == "visible" and (x, y) not in visible and (x, y) != (map_state.agent_x, map_state.agent_y):
+                    continue
+                
+                i = y * w + x
+                ct = map_state.cell_grid[i]
+                if ct == 1: grid[0, y, x] = 1.0 # Wall
+                elif ct == 2: grid[1, y, x] = 1.0 # Door Open
+                elif ct == 3: grid[2, y, x] = 1.0 # Door Closed
+                elif ct == 4: grid[3, y, x] = 1.0 # Exit
+                elif ct == 5: grid[4, y, x] = 1.0 # Obstacle
+                
+                grid[5, y, x] = float(map_state.fire_grid[i])
+                grid[6, y, x] = float(map_state.smoke_grid[i])
+        
+        # Global features
+        metadata = pyre_obs.metadata or {}
+        nearest_exit = float(metadata.get("nearest_exit_distance", 48) or 48.0) / 48.0
+        # smoke_level → is_coughing proxy (moderate/heavy smoke = coughing)
+        smoke = getattr(pyre_obs, "smoke_level", "none") or "none"
+        is_coughing = 1.0 if smoke in ("moderate", "heavy") else 0.0
+
+        global_feats = np.array([
+            float(pyre_obs.agent_health) / 100.0,
+            float(pyre_obs.agent_health) / 100.0,   # oxygen_level proxy
+            float(map_state.step_count) / float(map_state.max_steps),
+            float(map_state.fire_spread_rate),
+            float(map_state.humidity),
+            float(map_state.agent_x) / 24.0,
+            float(map_state.agent_y) / 24.0,
+            nearest_exit,
+            is_coughing,
+        ], dtype=np.float32)
+
+        # Heat sensor — derive 3×3 fire neighbourhood around agent from the fire grid
+        ax, ay = map_state.agent_x, map_state.agent_y
+        gw, gh = map_state.grid_w, map_state.grid_h
+        heat_vals = []
+        for dy in (-1, 0, 1):
+            for dx in (-1, 0, 1):
+                nx, ny = ax + dx, ay + dy
+                if 0 <= nx < gw and 0 <= ny < gh:
+                    heat_vals.append(float(map_state.fire_grid[ny * gw + nx]))
+                else:
+                    heat_vals.append(0.0)
+        heat = np.array(heat_vals, dtype=np.float32).reshape(1, 3, 3)
+        
+        return {
+            "grid": grid,
+            "global": global_feats,
+            "heat": heat
+        }
+
+    def reset(self, seed=None, options=None):
+        super().reset(seed=seed)
+        difficulty = options.get("difficulty", self.difficulty) if options else self.difficulty
+        pyre_obs = self.env.reset(seed=seed, difficulty=difficulty)
+        return self._get_obs(pyre_obs), {}
+
+    def step(self, action_idx):
+        # Map Discrete action to PyreAction
+        if action_idx < 4:
+            dirs = ["north", "south", "west", "east"]
+            action = PyreAction(action="move", direction=dirs[action_idx])
+        elif action_idx < 8:
+            dirs = ["north", "south", "west", "east"]
+            action = PyreAction(action="look", direction=dirs[action_idx - 4])
+        elif action_idx == 8:
+            action = PyreAction(action="wait")
+        elif action_idx < 9 + 16:
+            action = PyreAction(action="door", target_id=f"door_{action_idx - 8}", door_state="open")
+        else:
+            action = PyreAction(action="door", target_id=f"door_{action_idx - 24}", door_state="close")
+            
+        pyre_obs = self.env.step(action)
+        
+        obs = self._get_obs(pyre_obs)
+        reward = pyre_obs.reward
+        terminated = pyre_obs.done
+        truncated = False # Step limit handled by env.done
+        
+        return obs, reward, terminated, truncated, {"pyre_obs": pyre_obs}
+
+if __name__ == "__main__":
+    from stable_baselines3 import PPO
+    from stable_baselines3.common.callbacks import CheckpointCallback
+    import argparse
+    
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--episodes", type=int, default=1500, help="Total episodes to train across all levels")
+    parser.add_argument("--difficulty", type=str, default="curriculum", help="easy, medium, hard, random, or curriculum")
+    parser.add_argument("--output", type=str, default="artifacts/ppo_pyre_multilevel")
+    args = parser.parse_args()
+    
+    from gymnasium.wrappers import RecordEpisodeStatistics
+    
+    # Custom wrapper to handle difficulty changes
+    class MultiLevelWrapper(gym.Wrapper):
+        def __init__(self, env, mode="curriculum"):
+            super().__init__(env)
+            self.mode = mode
+            self.current_difficulty = "easy"
+            self.step_count = 0
+            self.total_steps = 0
+            
+        def reset(self, **kwargs):
+            if self.mode == "random":
+                self.current_difficulty = np.random.choice(["easy", "medium", "hard"])
+            elif self.mode == "curriculum":
+                if self.total_steps < 0.33 * total_training_steps:
+                    self.current_difficulty = "easy"
+                elif self.total_steps < 0.66 * total_training_steps:
+                    self.current_difficulty = "medium"
+                else:
+                    self.current_difficulty = "hard"
+            else:
+                self.current_difficulty = self.mode
+            
+            # Extract options from kwargs if present, or create new
+            options = kwargs.get("options")
+            if options is None:
+                options = {}
+            options["difficulty"] = self.current_difficulty
+            kwargs["options"] = options
+                
+            return self.env.reset(**kwargs)
+
+        def step(self, action):
+            obs, reward, term, trunc, info = self.env.step(action)
+            self.total_steps += 1
+            info["difficulty"] = self.current_difficulty
+            return obs, reward, term, trunc, info
+
+    total_training_steps = args.episodes * 60
+    
+    env = PyreGymEnv(difficulty="easy") # Base difficulty
+    env = MultiLevelWrapper(env, mode=args.difficulty)
+    env = RecordEpisodeStatistics(env)
+    
+    # Custom CNN policy for the grid
+    # Increased network capacity for multiple levels
+    policy_kwargs = dict(
+        activation_fn=th.nn.ReLU,
+        net_arch=dict(pi=[256, 128], qf=[256, 128])
+    )
+    
+    model = PPO(
+        "MultiInputPolicy",
+        env,
+        verbose=1,
+        learning_rate=2e-4, # Slightly lower LR for stability across levels
+        n_steps=2048,
+        batch_size=128,
+        n_epochs=10,
+        gamma=0.99,
+        gae_lambda=0.95,
+        clip_range=0.2,
+        ent_coef=0.02, # Higher entropy to encourage exploration in procedural maps
+    )
+    
+    print(f"Starting multi-level training (mode: {args.difficulty})...")
+    
+    # Add a simple callback to log episode rewards to a CSV
+    from stable_baselines3.common.callbacks import BaseCallback
+    import csv
+    from pathlib import Path
+    
+    class CSVLogCallback(BaseCallback):
+        def __init__(self, filename):
+            super().__init__()
+            self.filename = filename
+            self.results = []
+        def _on_step(self):
+            # Check every step for finished episodes
+            for info in self.locals.get("infos", []):
+                if "episode" in info:
+                    self.results.append({
+                        "step": self.num_timesteps,
+                        "reward": info["episode"]["r"],
+                        "length": info["episode"]["l"]
+                    })
+            return True
+        def _on_rollout_end(self):
+            # Save every rollout
+            if self.results:
+                with open(self.filename, "w", newline="") as f:
+                    writer = csv.DictWriter(f, fieldnames=["step", "reward", "length"])
+                    writer.writeheader()
+                    writer.writerows(self.results)
+            return True
+
+    csv_path = args.output + ".csv"
+    callback = CSVLogCallback(csv_path)
+
+    # CNN MultiInputPolicy needs far more steps than a flat MLP to warm up.
+    # episodes * 50 ≈ 15k steps (too few). Use episodes * 500 for meaningful learning.
+    model.learn(total_timesteps=args.episodes * 500, callback=callback)
+    
+    model.save(args.output)
+    print(f"Model saved to {args.output}")
+    print(f"Metrics saved to {csv_path}")
+
+    # Generate a quick SVG graph if we have results
+    if callback.results:
+        try:
+            from examples.train_rl_agent import save_training_graph
+            # Mocking the row format expected by the baseline plotter
+            rows = [{"episode": i, "reward": r["reward"], "evacuated": 0} for i, r in enumerate(callback.results)]
+            save_training_graph(Path(args.output + ".svg"), rows, [])
+            print(f"Graph saved to {args.output}.svg")
+        except Exception as e:
+            print(f"Could not generate SVG automatically: {e}")
+            print("CSV is available at " + csv_path)

examples/train_torch_ppo.py

@@ -0,0 +1,1278 @@
+"""
+PyTorch PPO Agent for Pyre — Fire Evacuation RL Training Script.
+
+=== ENVIRONMENT SUMMARY ===
+Pyre is a partial-observability crisis navigation environment:
+  - Grid: 16×16 (easy/medium) or 20×24 (hard, procedural)
+  - Agent: Spawns inside a burning building, must evacuate before dying
+  - Fire: Spreads via cellular automaton — wind, humidity, fuel vary per episode
+  - Partial observability: visibility radius (2–5 cells) shrinks in heavy smoke
+  - Doors: Can be opened/closed to slow fire spread (+0.5 strategic door bonus)
+  - Health: 100 HP, drains from smoke (0.5–5/step) and fire (10/step)
+
+=== ACTION SPACE (41 discrete) ===
+  0–3   : move(north|south|west|east)
+  4–7   : look(north|south|west|east)  — scan without moving, still costs a step
+  8     : wait()
+  9–24  : door(door_1..16, open)
+  25–40 : door(door_1..16, close)
+  Runtime action masking via `available_actions_hint` prevents invalid moves.
+
+=== OBSERVATION ENCODING ===
+  Per-step grid: 24×24 padded map × 10 channels
+    • 6 one-hot cell type (floor/wall/door_open/door_closed/exit/obstacle)
+    • fire intensity [0, 1]
+    • smoke density  [0, 1]
+    • visibility mask (1=visible, 0=unseen)
+    • agent position mask
+  Global scalars (22): health, step_progress, fire_spread, humidity,
+    agent_x, agent_y, exit_distance, reachable_exits, visible_cells,
+    fire_sources, smoke_severity, alive, evacuated, wind (one-hot 5), difficulty (one-hot 3)
+  Frame stacking: 4 consecutive frames → input_dim = 5782 × 4 = 23128
+
+=== REWARD STRUCTURE ===
+  Per-step:
+    -0.01  time penalty (urgency)
+    +0.10  BFS progress toward nearest unblocked exit
+    -0.05  regression (moved farther from exit)
+    +0.05  safe-progress bonus (progress through smoke-free cell)
+    -0.50  danger penalty (moved into smoke≥moderate or fire-adjacent)
+    -0.02×dmg health drain penalty
+    +0.50  strategic door close (adjacent to fire, once per door per episode)
+    +0.02  exploration bonus (first visit to cell)
+  Terminal:
+    +5.00  evacuation success
+    +1.50×(hp/100) health survival bonus (max +1.5)
+    -10.0  death
+    -5.00  timeout
+    0→+3.0 near-miss partial credit (based on closest exit approach)
+    +0.05×remaining_steps time bonus
+
+=== ALGORITHM: PPO (Proximal Policy Optimization) ===
+WHY PPO over alternatives:
+  • DQN    — Off-policy, harder credit assignment for sparse terminal rewards; no clean action masking
+  • A2C    — Simpler but no clipping → unstable on hard stochastic episodes
+  • SAC    — Designed for continuous spaces; discrete SAC works but adds complexity
+  • LSTM-PPO — Better for fully text-only obs; grid map_state already encodes spatial state
+  → PPO + frame-stack + action-mask hits the sweet spot for this env
+
+Key PPO improvements over the existing NumPy A2C (train_rl_agent.py):
+  ✓ PPO clip (ε=0.2)        prevents catastrophic updates
+  ✓ Entropy regularization  sustains exploration in smoke-obscured corridors
+  ✓ Value function clipping  stabilises critic under sparse terminal rewards
+  ✓ GPU acceleration         10–20× faster than NumPy baseline
+  ✓ LayerNorm in network     improves gradient flow for large input dims
+  ✓ Linear LR decay          stabilises late-stage convergence
+  ✓ Better curriculum        3-stage easy→medium→hard with patience gating
+
+Usage:
+    python examples/train_torch_ppo.py --episodes 500 --device cuda
+    python examples/train_torch_ppo.py --episodes 300 --difficulty-schedule easy,medium,hard
+    python examples/train_torch_ppo.py --resume artifacts/pyre_ppo_checkpoint.pt
+    python examples/train_torch_ppo.py --describe-only
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import os
+import sys
+import time
+from collections import deque
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import numpy as np
+
+# ---------------------------------------------------------------------------
+# Optional torch import — fail fast with a helpful message
+# ---------------------------------------------------------------------------
+try:
+    import torch
+    import torch.nn as nn
+    import torch.nn.functional as F
+    from torch.optim import Adam
+    from torch.optim.lr_scheduler import LinearLR
+except ImportError:
+    sys.exit(
+        "PyTorch not found. Install with:\n"
+        "  pip install torch --index-url https://download.pytorch.org/whl/cu121\n"
+        "or for CPU only:\n"
+        "  pip install torch"
+    )
+
+# ---------------------------------------------------------------------------
+# Project imports — support both package install and direct run from root
+# ---------------------------------------------------------------------------
+_ROOT = Path(__file__).resolve().parent.parent
+if str(_ROOT) not in sys.path:
+    sys.path.insert(0, str(_ROOT))
+
+try:
+    from pyre_env.models import PyreAction, PyreObservation
+    from pyre_env.server.pyre_env_environment import PyreEnvironment
+except ModuleNotFoundError:
+    try:
+        from models import PyreAction, PyreObservation
+        from server.pyre_env_environment import PyreEnvironment
+    except ModuleNotFoundError:
+        sys.exit(
+            "Cannot import Pyre modules. Run this script from the openenv-pyre root:\n"
+            "  python examples/train_torch_ppo.py"
+        )
+
+# ---------------------------------------------------------------------------
+# Reuse the established observation/action interface from train_rl_agent.py
+# These are the canonical definitions for this environment.
+# ---------------------------------------------------------------------------
+MAX_GRID_W = 24
+MAX_GRID_H = 24
+MAX_DOORS = 16
+DIRECTIONS = ("north", "south", "west", "east")
+WINDS = ("CALM", "NORTH", "SOUTH", "WEST", "EAST")
+DIFFICULTIES = ("easy", "medium", "hard")
+
+MOVE_KEYS = [f"move(direction='{d}')" for d in DIRECTIONS]
+LOOK_KEYS = [f"look(direction='{d}')" for d in DIRECTIONS]
+WAIT_KEY = "wait()"
+OPEN_KEYS = [f"door(target_id='door_{i}', door_state='open')" for i in range(1, MAX_DOORS + 1)]
+CLOSE_KEYS = [f"door(target_id='door_{i}', door_state='close')" for i in range(1, MAX_DOORS + 1)]
+ACTION_KEYS = MOVE_KEYS + LOOK_KEYS + [WAIT_KEY] + OPEN_KEYS + CLOSE_KEYS
+ACTION_DIM = len(ACTION_KEYS)  # 41
+ACTION_TO_INDEX = {key: idx for idx, key in enumerate(ACTION_KEYS)}
+
+import re
+_MOVE_RE = re.compile(r"move\(direction='(north|south|west|east)'\)")
+_LOOK_RE = re.compile(r"look\(direction='(north|south|west|east)'\)")
+_DOOR_RE = re.compile(r"door\(target_id='(door_(\d+))', door_state='(open|close)'\)")
+
+
+def action_index_to_env_action(index: int) -> PyreAction:
+    if 0 <= index < 4:
+        return PyreAction(action="move", direction=DIRECTIONS[index])
+    if 4 <= index < 8:
+        return PyreAction(action="look", direction=DIRECTIONS[index - 4])
+    if index == 8:
+        return PyreAction(action="wait")
+    if 9 <= index < 9 + MAX_DOORS:
+        door_id = f"door_{index - 8}"
+        return PyreAction(action="door", target_id=door_id, door_state="open")
+    door_slot = index - (9 + MAX_DOORS)
+    door_id = f"door_{door_slot + 1}"
+    return PyreAction(action="door", target_id=door_id, door_state="close")
+
+
+def build_action_mask(observation: PyreObservation, exclude_look: bool = True) -> np.ndarray:
+    """Build a binary validity mask over the 41-action space.
+
+    exclude_look=True (default for RL):
+        Suppresses all 4 'look' actions. The RL agent already receives the full
+        grid via map_state — look gives zero new information but wastes a step
+        and earns no reward. Excluding it concentrates the policy on moves and
+        doors, which are the only actions that can improve the agent's position.
+    """
+    mask = np.zeros(ACTION_DIM, dtype=np.float32)
+    for hint in observation.available_actions_hint:
+        idx = ACTION_TO_INDEX.get(hint)
+        if idx is not None:
+            mask[idx] = 1.0
+            continue
+        m = _MOVE_RE.fullmatch(hint)
+        if m:
+            mask[ACTION_TO_INDEX[f"move(direction='{m.group(1)}')"]] = 1.0
+            continue
+        m = _LOOK_RE.fullmatch(hint)
+        if m:
+            if not exclude_look:
+                mask[ACTION_TO_INDEX[f"look(direction='{m.group(1)}')"]] = 1.0
+            continue
+        m = _DOOR_RE.fullmatch(hint)
+        if m:
+            door_id, door_num, state = m.group(1), int(m.group(2)), m.group(3)
+            if 1 <= door_num <= MAX_DOORS:
+                mask[ACTION_TO_INDEX[f"door(target_id='{door_id}', door_state='{state}')"]] = 1.0
+    if mask.sum() == 0:
+        mask[ACTION_TO_INDEX[WAIT_KEY]] = 1.0
+    return mask
+
+
+class ObservationEncoder:
+    """Encode PyreObservation into a fixed-length float32 vector.
+
+    Mode 'visible': only populate cells within the agent's sight radius —
+        mimics true partial observability; preferred for training.
+    Mode 'full': expose complete ground-truth grid — useful for debugging
+        or oracle upper-bound experiments.
+
+    Output shape: (base_dim,) = (MAX_GRID_W × MAX_GRID_H × 10 + 25,) = (5785,)
+    With history stacking of k frames: (5785 × k,)
+
+    The 3 extra scalars over the v1 baseline are map-agnostic exit-compass
+    features (Fix 3): exit_dx_norm, exit_dy_norm, exit_manhattan_norm.
+    These allow the agent to locate the nearest exit on procedurally generated
+    maps without having to memorise layout-specific coordinates.
+    """
+
+    base_dim = MAX_GRID_W * MAX_GRID_H * 10 + 25
+
+    def __init__(self, mode: str = "visible"):
+        if mode not in {"visible", "full"}:
+            raise ValueError(f"mode must be 'visible' or 'full', got '{mode}'")
+        self.mode = mode
+
+    def encode(self, observation: PyreObservation) -> np.ndarray:
+        ms = observation.map_state
+        if ms is None:
+            raise ValueError("map_state is required for encoding.")
+
+        cell_one_hot = np.zeros((MAX_GRID_H, MAX_GRID_W, 6), dtype=np.float32)
+        fire_ch = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+        smoke_ch = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+        vis_ch = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+        agent_ch = np.zeros((MAX_GRID_H, MAX_GRID_W), dtype=np.float32)
+
+        visible = {(x, y) for x, y in ms.visible_cells}
+        for y in range(ms.grid_h):
+            for x in range(ms.grid_w):
+                if self.mode == "visible" and (x, y) not in visible and (x, y) != (ms.agent_x, ms.agent_y):
+                    continue
+                i = y * ms.grid_w + x
+                ct = int(ms.cell_grid[i])
+                if 0 <= ct <= 5:
+                    cell_one_hot[y, x, ct] = 1.0
+                fire_ch[y, x] = float(ms.fire_grid[i])
+                smoke_ch[y, x] = float(ms.smoke_grid[i])
+                vis_ch[y, x] = 1.0 if (x, y) in visible else 0.0
+
+        if 0 <= ms.agent_x < MAX_GRID_W and 0 <= ms.agent_y < MAX_GRID_H:
+            agent_ch[ms.agent_y, ms.agent_x] = 1.0
+
+        grid_features = np.concatenate([
+            cell_one_hot.reshape(-1),
+            fire_ch.reshape(-1),
+            smoke_ch.reshape(-1),
+            vis_ch.reshape(-1),
+            agent_ch.reshape(-1),
+        ])
+
+        meta = observation.metadata or {}
+        wind = str(meta.get("wind_dir", ms.wind_dir or "CALM")).upper()
+        diff = str(meta.get("difficulty", "medium")).lower()
+        wi = WINDS.index(wind) if wind in WINDS else 0
+        di = DIFFICULTIES.index(diff) if diff in DIFFICULTIES else 1
+
+        wind_oh = np.zeros(len(WINDS), dtype=np.float32); wind_oh[wi] = 1.0
+        diff_oh = np.zeros(len(DIFFICULTIES), dtype=np.float32); diff_oh[di] = 1.0
+
+        # Fix 3 — map-agnostic exit compass features.
+        # Compute the direction vector and normalised Manhattan distance to the
+        # nearest exit cell (cell_type == 4) directly from the live grid.
+        # This gives the agent an exit "compass" that works on procedurally
+        # generated maps without memorising any layout.
+        EXIT_CELL_TYPE = 4
+        ax, ay = ms.agent_x, ms.agent_y
+        gw, gh = ms.grid_w, ms.grid_h
+        best_dist = float(gw + gh)
+        best_dx = 0.0
+        best_dy = 0.0
+        for cy in range(gh):
+            for cx in range(gw):
+                if int(ms.cell_grid[cy * gw + cx]) == EXIT_CELL_TYPE:
+                    d = abs(cx - ax) + abs(cy - ay)
+                    if d < best_dist:
+                        best_dist = d
+                        best_dx = float(cx - ax) / max(1, gw - 1)
+                        best_dy = float(cy - ay) / max(1, gh - 1)
+        exit_manhattan_norm = best_dist / float(gw + gh)
+
+        global_features = np.array([
+            float(observation.agent_health) / 100.0,
+            float(ms.agent_health) / 100.0,
+            float(ms.step_count) / max(1, ms.max_steps),
+            float(ms.fire_spread_rate),
+            float(ms.humidity),
+            float(ms.agent_x) / max(1, ms.grid_w - 1),
+            float(ms.agent_y) / max(1, ms.grid_h - 1),
+            float(meta.get("nearest_exit_distance", MAX_GRID_W + MAX_GRID_H) or 0.0) / float(MAX_GRID_W + MAX_GRID_H),
+            float(meta.get("reachable_exit_count", 0.0)) / 4.0,
+            float(meta.get("visible_cell_count", 0.0)) / float(MAX_GRID_W * MAX_GRID_H),
+            float(meta.get("fire_sources", 0.0)) / 5.0,
+            {"none": 0.0, "light": 0.33, "moderate": 0.66, "heavy": 1.0}.get(observation.smoke_level, 0.0),
+            1.0 if ms.agent_alive else 0.0,
+            1.0 if ms.agent_evacuated else 0.0,
+            # Fix 3: exit-compass (3 new scalars — map-agnostic, layout-independent)
+            best_dx,           # signed x-direction toward nearest exit
+            best_dy,           # signed y-direction toward nearest exit
+            exit_manhattan_norm,  # how far away the exit is (0 = here, 1 = max)
+        ], dtype=np.float32)
+
+        return np.concatenate([grid_features, global_features, wind_oh, diff_oh]).astype(np.float32)
+
+
+# ---------------------------------------------------------------------------
+# Neural Network
+# ---------------------------------------------------------------------------
+
+class ActorCritic(nn.Module):
+    """Shared-backbone Actor-Critic network for PPO.
+
+    Architecture:
+        Input → LayerNorm → FC(512) → LayerNorm → ReLU
+                          → FC(256) → LayerNorm → ReLU
+                          → FC(128) → ReLU
+               ┌──────────────┴──────────────┐
+         Policy head (→ logits)        Value head (→ scalar)
+
+    LayerNorm before activations improves gradient flow for the large
+    (23128-dim) flat input without requiring feature normalization.
+    """
+
+    def __init__(self, input_dim: int, action_dim: int, hidden_sizes: Tuple[int, ...] = (512, 256, 128)):
+        super().__init__()
+        h1, h2, h3 = hidden_sizes
+
+        self.shared = nn.Sequential(
+            nn.LayerNorm(input_dim),
+            nn.Linear(input_dim, h1),
+            nn.LayerNorm(h1),
+            nn.ReLU(),
+            nn.Linear(h1, h2),
+            nn.LayerNorm(h2),
+            nn.ReLU(),
+            nn.Linear(h2, h3),
+            nn.ReLU(),
+        )
+
+        # Orthogonal init — standard for PPO (improves early convergence)
+        self._init_orthogonal()
+
+        self.policy_head = nn.Linear(h3, action_dim)
+        self.value_head = nn.Linear(h3, 1)
+
+        # Small init for output heads prevents saturated softmax early on
+        nn.init.orthogonal_(self.policy_head.weight, gain=0.01)
+        nn.init.zeros_(self.policy_head.bias)
+        nn.init.orthogonal_(self.value_head.weight, gain=1.0)
+        nn.init.zeros_(self.value_head.bias)
+
+    def _init_orthogonal(self) -> None:
+        for layer in self.shared:
+            if isinstance(layer, nn.Linear):
+                nn.init.orthogonal_(layer.weight, gain=np.sqrt(2))
+                nn.init.zeros_(layer.bias)
+
+    def forward(
+        self,
+        obs: torch.Tensor,
+        mask: torch.Tensor,
+    ) -> Tuple[torch.distributions.Categorical, torch.Tensor]:
+        """
+        Args:
+            obs:  (B, input_dim) float32
+            mask: (B, action_dim) float32  — 1.0 = valid, 0.0 = invalid
+        Returns:
+            dist:   Categorical distribution (action masking applied as -inf)
+            values: (B,) float32
+        """
+        features = self.shared(obs)
+        logits = self.policy_head(features)
+
+        # Mask invalid actions with -inf before softmax (numerically stable)
+        logits = torch.where(mask.bool(), logits, torch.full_like(logits, -1e9))
+
+        dist = torch.distributions.Categorical(logits=logits)
+        values = self.value_head(features).squeeze(-1)
+        return dist, values
+
+    def act(
+        self,
+        obs: torch.Tensor,
+        mask: torch.Tensor,
+        deterministic: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Sample (or take greedy) action. Returns (action, log_prob, value)."""
+        dist, values = self(obs, mask)
+        action = dist.mode if deterministic else dist.sample()
+        log_prob = dist.log_prob(action)
+        return action, log_prob, values
+
+    def evaluate(
+        self,
+        obs: torch.Tensor,
+        mask: torch.Tensor,
+        action: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Evaluate stored actions during PPO update. Returns (log_prob, value, entropy)."""
+        dist, values = self(obs, mask)
+        log_prob = dist.log_prob(action)
+        entropy = dist.entropy()
+        return log_prob, values, entropy
+
+
+# ---------------------------------------------------------------------------
+# Rollout buffer
+# ---------------------------------------------------------------------------
+
+@dataclass
+class RolloutBuffer:
+    """Stores transitions for a batch of episodes before PPO update."""
+    obs: List[np.ndarray] = field(default_factory=list)
+    masks: List[np.ndarray] = field(default_factory=list)
+    actions: List[int] = field(default_factory=list)
+    rewards: List[float] = field(default_factory=list)
+    log_probs: List[float] = field(default_factory=list)
+    values: List[float] = field(default_factory=list)
+    dones: List[bool] = field(default_factory=list)
+
+    def clear(self) -> None:
+        self.obs.clear()
+        self.masks.clear()
+        self.actions.clear()
+        self.rewards.clear()
+        self.log_probs.clear()
+        self.values.clear()
+        self.dones.clear()
+
+    def __len__(self) -> int:
+        return len(self.rewards)
+
+
+# ---------------------------------------------------------------------------
+# GAE computation
+# ---------------------------------------------------------------------------
+
+def compute_gae(
+    rewards: np.ndarray,
+    values: np.ndarray,
+    dones: np.ndarray,
+    gamma: float,
+    gae_lambda: float,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Generalized Advantage Estimation.
+
+    Returns (returns, advantages) — both shape (T,).
+    Episode boundaries (done=True) reset the GAE accumulator so advantages
+    don't bleed across episodes within a mixed batch.
+    """
+    T = len(rewards)
+    advantages = np.zeros(T, dtype=np.float32)
+    gae = 0.0
+    next_value = 0.0
+    for t in reversed(range(T)):
+        if dones[t]:
+            next_value = 0.0
+            gae = 0.0
+        delta = rewards[t] + gamma * next_value * (1.0 - dones[t]) - values[t]
+        gae = delta + gamma * gae_lambda * (1.0 - dones[t]) * gae
+        advantages[t] = gae
+        next_value = values[t]
+    returns = advantages + values
+    return returns, advantages
+
+
+# ---------------------------------------------------------------------------
+# Episode runner
+# ---------------------------------------------------------------------------
+
+@dataclass
+class EpisodeResult:
+    total_reward: float
+    steps: int
+    evacuated: bool
+    final_health: float
+    difficulty: str
+
+
+def run_episode(
+    env: PyreEnvironment,
+    network: ActorCritic,
+    encoder: ObservationEncoder,
+    device: torch.device,
+    difficulty: str,
+    history_length: int,
+    buffer: RolloutBuffer,
+    deterministic: bool = False,
+) -> EpisodeResult:
+    """Run one episode, appending transitions to *buffer*."""
+    observation = env.reset(difficulty=difficulty)
+    zero_frame = np.zeros(encoder.base_dim, dtype=np.float32)
+    frames: deque = deque([zero_frame.copy() for _ in range(history_length)], maxlen=history_length)
+    frames.append(encoder.encode(observation))
+
+    total_reward = 0.0
+    final_health = observation.agent_health
+    evacuated = False
+    steps = 0
+    # Anti-loop tracking: remember the last LOOP_WINDOW positions this episode.
+    # Revisiting any of them means the agent is circling, not exploring.
+    LOOP_WINDOW = 12
+    recent_positions: deque = deque(maxlen=LOOP_WINDOW)
+
+    network.eval()
+    with torch.no_grad():
+        while True:
+            state_vec = np.concatenate(list(frames), dtype=np.float32)
+            # exclude_look=True: RL agent sees full grid — look wastes steps
+            action_mask = build_action_mask(observation, exclude_look=True)
+
+            obs_t = torch.tensor(state_vec, dtype=torch.float32, device=device).unsqueeze(0)
+            mask_t = torch.tensor(action_mask, dtype=torch.float32, device=device).unsqueeze(0)
+
+            action_t, log_prob_t, value_t = network.act(obs_t, mask_t, deterministic=deterministic)
+
+            action_idx = int(action_t.item())
+            env_action = action_index_to_env_action(action_idx)
+            next_obs = env.step(env_action)
+
+            reward = float(next_obs.reward or 0.0)
+
+            # ----------------------------------------------------------------
+            # Reward shaping 1 — idle penalty
+            # The env's -0.01/step is too weak; make waiting explicitly costly.
+            # ----------------------------------------------------------------
+            chosen_action = env_action.action
+            if chosen_action == "wait":
+                reward -= 0.05
+
+            # ----------------------------------------------------------------
+            # Reward shaping 2 — fire-approach penalty (Fix 2)
+            # Penalise landing on (or moving next to) a cell with active fire.
+            # This is stronger than the env's DangerPenalty and fires *before*
+            # health drain accumulates, teaching the agent to predict spread.
+            # We look at the NEW observation's map to catch the current step.
+            # ----------------------------------------------------------------
+            ms_next = next_obs.map_state
+            if ms_next is not None and chosen_action.startswith("move"):
+                ax, ay = ms_next.agent_x, ms_next.agent_y
+                gw, gh = ms_next.grid_w, ms_next.grid_h
+                fire_grid = ms_next.fire_grid
+                for dx, dy in ((0, 1), (0, -1), (1, 0), (-1, 0)):
+                    nx, ny = ax + dx, ay + dy
+                    if 0 <= nx < gw and 0 <= ny < gh:
+                        if float(fire_grid[ny * gw + nx]) > 0.15:
+                            reward -= 0.15  # early fire-proximity warning
+                            break
+
+            # ----------------------------------------------------------------
+            # Reward shaping 3 — anti-loop penalty
+            # If the agent steps onto a cell it occupied in the last LOOP_WINDOW
+            # steps, it is circling. Penalise to force forward exploration.
+            # Fires only on move actions — wait is already penalised above.
+            # ----------------------------------------------------------------
+            if ms_next is not None and chosen_action.startswith("move"):
+                cur_pos = (ms_next.agent_x, ms_next.agent_y)
+                if cur_pos in recent_positions:
+                    reward -= 0.2  # break the loop
+                recent_positions.append(cur_pos)
+
+            done = bool(next_obs.done)
+
+            buffer.obs.append(state_vec)
+            buffer.masks.append(action_mask)
+            buffer.actions.append(action_idx)
+            buffer.rewards.append(reward)
+            buffer.log_probs.append(float(log_prob_t.item()))
+            buffer.values.append(float(value_t.item()))
+            buffer.dones.append(done)
+
+            total_reward += reward
+            steps += 1
+            final_health = next_obs.agent_health
+            evacuated = next_obs.agent_evacuated
+
+            frames.append(encoder.encode(next_obs))
+            observation = next_obs
+            if done:
+                break
+
+    return EpisodeResult(
+        total_reward=total_reward,
+        steps=steps,
+        evacuated=evacuated,
+        final_health=final_health,
+        difficulty=difficulty,
+    )
+
+
+# ---------------------------------------------------------------------------
+# PPO update
+# ---------------------------------------------------------------------------
+
+def ppo_update(
+    network: ActorCritic,
+    optimizer: Adam,
+    buffer: RolloutBuffer,
+    device: torch.device,
+    clip_eps: float,
+    value_clip_eps: float,
+    entropy_coef: float,
+    value_coef: float,
+    n_epochs: int,
+    minibatch_size: int,
+    gamma: float,
+    gae_lambda: float,
+    max_grad_norm: float,
+) -> Dict[str, float]:
+    """Full PPO update over the collected rollout buffer."""
+    rewards = np.array(buffer.rewards, dtype=np.float32)
+    values = np.array(buffer.values, dtype=np.float32)
+    dones = np.array(buffer.dones, dtype=np.float32)
+
+    returns, advantages = compute_gae(rewards, values, dones, gamma, gae_lambda)
+
+    # Normalize advantages across the whole batch (reduces variance)
+    advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+
+    obs_arr = torch.tensor(np.stack(buffer.obs), dtype=torch.float32, device=device)
+    mask_arr = torch.tensor(np.stack(buffer.masks), dtype=torch.float32, device=device)
+    action_arr = torch.tensor(buffer.actions, dtype=torch.long, device=device)
+    old_logp_arr = torch.tensor(buffer.log_probs, dtype=torch.float32, device=device)
+    return_arr = torch.tensor(returns, dtype=torch.float32, device=device)
+    adv_arr = torch.tensor(advantages, dtype=torch.float32, device=device)
+    old_value_arr = torch.tensor(values, dtype=torch.float32, device=device)
+
+    T = len(buffer)
+    metrics = {"policy_loss": 0.0, "value_loss": 0.0, "entropy": 0.0, "approx_kl": 0.0, "clip_frac": 0.0}
+    n_updates = 0
+
+    network.train()
+    for _ in range(n_epochs):
+        perm = torch.randperm(T, device=device)
+        for start in range(0, T, minibatch_size):
+            idx = perm[start:start + minibatch_size]
+            if len(idx) < 2:
+                continue
+
+            log_prob, value, entropy = network.evaluate(obs_arr[idx], mask_arr[idx], action_arr[idx])
+
+            # PPO ratio and clipped surrogate loss
+            ratio = torch.exp(log_prob - old_logp_arr[idx])
+            adv_mb = adv_arr[idx]
+            surr1 = ratio * adv_mb
+            surr2 = torch.clamp(ratio, 1.0 - clip_eps, 1.0 + clip_eps) * adv_mb
+            policy_loss = -torch.min(surr1, surr2).mean()
+
+            # Value loss with optional clipping (stabilises critic)
+            ret_mb = return_arr[idx]
+            old_val_mb = old_value_arr[idx]
+            value_pred_clipped = old_val_mb + torch.clamp(value - old_val_mb, -value_clip_eps, value_clip_eps)
+            value_loss = torch.max(
+                F.mse_loss(value, ret_mb),
+                F.mse_loss(value_pred_clipped, ret_mb),
+            )
+
+            entropy_loss = -entropy.mean()
+
+            loss = policy_loss + value_coef * value_loss + entropy_coef * entropy_loss
+
+            optimizer.zero_grad()
+            loss.backward()
+            nn.utils.clip_grad_norm_(network.parameters(), max_grad_norm)
+            optimizer.step()
+
+            with torch.no_grad():
+                approx_kl = ((ratio - 1) - (log_prob - old_logp_arr[idx])).mean().item()
+                clip_frac = ((ratio - 1.0).abs() > clip_eps).float().mean().item()
+
+            metrics["policy_loss"] += policy_loss.item()
+            metrics["value_loss"] += value_loss.item()
+            metrics["entropy"] += entropy.mean().item()
+            metrics["approx_kl"] += approx_kl
+            metrics["clip_frac"] += clip_frac
+            n_updates += 1
+
+    if n_updates > 0:
+        for k in metrics:
+            metrics[k] /= n_updates
+    return metrics
+
+
+# ---------------------------------------------------------------------------
+# Evaluation
+# ---------------------------------------------------------------------------
+
+def evaluate_policy(
+    env: PyreEnvironment,
+    network: ActorCritic,
+    encoder: ObservationEncoder,
+    device: torch.device,
+    difficulty: str,
+    history_length: int,
+    n_episodes: int,
+) -> Dict[str, float]:
+    rewards, successes, steps = [], [], []
+    dummy_buffer = RolloutBuffer()
+    for _ in range(n_episodes):
+        result = run_episode(
+            env=env, network=network, encoder=encoder, device=device,
+            difficulty=difficulty, history_length=history_length,
+            buffer=dummy_buffer, deterministic=True,
+        )
+        dummy_buffer.clear()
+        rewards.append(result.total_reward)
+        successes.append(float(result.evacuated))
+        steps.append(result.steps)
+    return {
+        "reward_mean": float(np.mean(rewards)),
+        "reward_max": float(np.max(rewards)),
+        "success_rate": float(np.mean(successes)),
+        "steps_mean": float(np.mean(steps)),
+    }
+
+
+# ---------------------------------------------------------------------------
+# PNG graph (matplotlib)
+# ---------------------------------------------------------------------------
+
+def save_training_graph_png(
+    path: Path,
+    episode_rows: List[Dict],
+    eval_rows: List[Dict],
+    window: int = 20,
+) -> None:
+    """Save a publication-quality PNG training graph with dual Y-axes."""
+    try:
+        import matplotlib
+        matplotlib.use("Agg")   # non-interactive backend — no display needed
+        import matplotlib.pyplot as plt
+        import matplotlib.ticker as mticker
+    except ImportError:
+        print("[warn] matplotlib not installed — skipping PNG graph. Run: uv pip install matplotlib")
+        return
+
+    if not episode_rows:
+        return
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    episodes   = [int(r["episode"]) for r in episode_rows]
+    rewards    = [float(r["reward"]) for r in episode_rows]
+    evacuated  = [float(r["evacuated"]) for r in episode_rows]
+    difficulty = [str(r["difficulty"]) for r in episode_rows]
+
+    # Moving average helper
+    def ma(values: list, w: int) -> list:
+        out, run, q = [], 0.0, []
+        for v in values:
+            q.append(v); run += v
+            if len(q) > w: run -= q.pop(0)
+            out.append(run / len(q))
+        return out
+
+    reward_ma  = ma(rewards, window)
+    success_ma = ma(evacuated, window)
+
+    eval_eps  = [int(r["episode"])      for r in eval_rows]
+    eval_succ = [float(r["success_rate"]) for r in eval_rows]
+
+    # Difficulty shading regions
+    diff_colors = {"easy": "#d4edda", "medium": "#fff3cd", "hard": "#f8d7da"}
+    regions: List[tuple] = []
+    if difficulty:
+        cur, start = difficulty[0], episodes[0]
+        for ep, d in zip(episodes[1:], difficulty[1:]):
+            if d != cur:
+                regions.append((start, ep, cur))
+                cur, start = d, ep
+        regions.append((start, episodes[-1], cur))
+
+    fig, ax1 = plt.subplots(figsize=(14, 6))
+    ax2 = ax1.twinx()
+
+    # Shade difficulty regions
+    for x0, x1, diff in regions:
+        ax1.axvspan(x0, x1, color=diff_colors.get(diff, "#eeeeee"), alpha=0.35, zorder=0)
+
+    # Zero line
+    ax1.axhline(0, color="#aaaaaa", linewidth=0.8, linestyle="--", zorder=1)
+
+    # Raw reward (faint)
+    ax1.plot(episodes, rewards, color="#d1c7bc", linewidth=0.8,
+             alpha=0.6, label="Episode reward", zorder=2)
+
+    # Reward moving average
+    ax1.plot(episodes, reward_ma, color="#c1661c", linewidth=2.5,
+             label=f"Reward (MA-{window})", zorder=3)
+
+    # Success moving average (right axis)
+    ax2.plot(episodes, success_ma, color="#1a7a8a", linewidth=2.5,
+             linestyle="-", label=f"Success rate (MA-{window})", zorder=3)
+
+    # Eval checkpoints
+    if eval_eps:
+        ax2.scatter(eval_eps, eval_succ, color="#0d5b6b", s=60, zorder=5,
+                    marker="D", label="Eval success", edgecolors="white", linewidths=1.2)
+
+    # Axes labels & formatting
+    ax1.set_xlabel("Episode", fontsize=13, fontweight="bold", labelpad=8)
+    ax1.set_ylabel("Reward", fontsize=13, fontweight="bold", color="#c1661c", labelpad=8)
+    ax2.set_ylabel("Success Rate", fontsize=13, fontweight="bold", color="#1a7a8a", labelpad=8)
+
+    ax1.tick_params(axis="y", labelcolor="#c1661c")
+    ax2.tick_params(axis="y", labelcolor="#1a7a8a")
+    ax2.set_ylim(-0.05, 1.05)
+    ax2.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1.0, decimals=0))
+
+    ax1.grid(True, which="major", linestyle="--", linewidth=0.6,
+             color="#dddddd", alpha=0.8, zorder=0)
+    ax1.set_xlim(episodes[0], episodes[-1])
+
+    ax1.tick_params(axis="x", labelsize=10)
+    ax1.tick_params(axis="y", labelsize=10)
+    ax2.tick_params(axis="y", labelsize=10)
+
+    # Title
+    total_eps = episodes[-1]
+    final_sr  = success_ma[-1] if success_ma else 0.0
+    fig.suptitle(
+        f"Pyre PPO Training  —  {total_eps} episodes  |  final success rate: {final_sr:.0%}",
+        fontsize=14, fontweight="bold", y=1.01,
+    )
+
+    # Difficulty legend patches
+    import matplotlib.patches as mpatches
+    diff_patches = [
+        mpatches.Patch(color=diff_colors[d], alpha=0.6, label=d.capitalize())
+        for d in ["easy", "medium", "hard"] if any(r == d for r in difficulty)
+    ]
+
+    # Combine legends from both axes
+    h1, l1 = ax1.get_legend_handles_labels()
+    h2, l2 = ax2.get_legend_handles_labels()
+    ax1.legend(h1 + h2 + diff_patches, l1 + l2 + [p.get_label() for p in diff_patches],
+               loc="upper left", fontsize=9, framealpha=0.85)
+
+    fig.tight_layout()
+    fig.savefig(path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+
+
+# ---------------------------------------------------------------------------
+# Curriculum scheduling
+# ---------------------------------------------------------------------------
+
+def build_curriculum(schedule_str: str, n_episodes: int) -> List[str]:
+    """Expand comma-separated difficulty stages evenly over n_episodes.
+
+    Example: 'easy,medium,hard' with 300 episodes → 100 each.
+    Used only when patience_threshold=0 (static schedule).
+    """
+    stages = [s.strip().lower() for s in schedule_str.split(",") if s.strip()]
+    if not stages:
+        stages = ["medium"]
+    for s in stages:
+        if s not in DIFFICULTIES:
+            raise ValueError(f"Unknown difficulty '{s}'. Choose from {DIFFICULTIES}.")
+    seg = max(1, n_episodes // len(stages))
+    schedule = []
+    for s in stages:
+        schedule.extend([s] * seg)
+    while len(schedule) < n_episodes:
+        schedule.append(stages[-1])
+    return schedule[:n_episodes]
+
+
+class PatienceCurriculum:
+    """Dynamic difficulty scheduler that gates advancement on sustained success rate.
+
+    Stays on current difficulty until success_rate_30 >= threshold for
+    patience_window consecutive episodes, then advances to the next stage.
+    During the hard phase an optional mix_ratio fraction of episodes are
+    replayed on the previous (medium) difficulty to prevent catastrophic
+    forgetting of the medium policy.
+
+    Args:
+        stages:           ordered list of difficulty strings, e.g. ['easy','medium','hard']
+        threshold:        minimum success rate (0–1) required before advancing
+        patience_window:  number of consecutive episodes that must meet threshold
+        mix_ratio:        fraction of hard-phase episodes to run on medium instead (0–1)
+    """
+
+    def __init__(
+        self,
+        stages: List[str],
+        threshold: float,
+        patience_window: int,
+        mix_ratio: float = 0.0,
+    ) -> None:
+        self.stages = stages
+        self.threshold = threshold
+        self.patience_window = patience_window
+        self.mix_ratio = mix_ratio
+        self.stage_idx = 0
+        self._streak = 0
+
+    @property
+    def current(self) -> str:
+        return self.stages[self.stage_idx]
+
+    def step(self, success_rate_30: float) -> str:
+        """Call once per episode *after* appending to success_window.
+
+        Returns the difficulty to use for the *next* episode.
+        Also handles the hard-phase medium-mix injection.
+        """
+        if self.stage_idx < len(self.stages) - 1:
+            if success_rate_30 >= self.threshold:
+                self._streak += 1
+            else:
+                self._streak = 0
+            if self._streak >= self.patience_window:
+                self.stage_idx += 1
+                self._streak = 0
+                print(
+                    f"  [curriculum] Advanced to '{self.current}' "
+                    f"(success_rate_30={success_rate_30:.2f} >= {self.threshold} "
+                    f"for {self.patience_window} eps)"
+                )
+
+        # Hard-phase mix: occasionally replay medium to prevent forgetting
+        if self.current == "hard" and self.mix_ratio > 0.0 and len(self.stages) >= 2:
+            prev = self.stages[self.stage_idx - 1]
+            if np.random.rand() < self.mix_ratio:
+                return prev  # medium replay episode
+        return self.current
+
+
+# ---------------------------------------------------------------------------
+# Checkpoint
+# ---------------------------------------------------------------------------
+
+def save_checkpoint(
+    path: Path,
+    network: ActorCritic,
+    optimizer: Adam,
+    scheduler,
+    episode: int,
+    args: argparse.Namespace,
+) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    torch.save({
+        "episode": episode,
+        "network_state": network.state_dict(),
+        "optimizer_state": optimizer.state_dict(),
+        "scheduler_state": scheduler.state_dict() if scheduler else None,
+        "args": vars(args),
+    }, path)
+
+
+def load_checkpoint(
+    path: Path,
+    network: ActorCritic,
+    optimizer: Adam,
+    scheduler,
+) -> int:
+    ckpt = torch.load(path, map_location="cpu", weights_only=False)
+    network.load_state_dict(ckpt["network_state"])
+    optimizer.load_state_dict(ckpt["optimizer_state"])
+    if scheduler and ckpt.get("scheduler_state"):
+        scheduler.load_state_dict(ckpt["scheduler_state"])
+    start_episode = int(ckpt.get("episode", 0))
+    print(f"[resume] Loaded checkpoint from episode {start_episode}: {path}")
+    return start_episode
+
+
+# ---------------------------------------------------------------------------
+# CSV logging
+# ---------------------------------------------------------------------------
+
+def save_csv(path: Path, rows: List[Dict]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    if not rows:
+        return
+    with path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+# ---------------------------------------------------------------------------
+# Main training loop
+# ---------------------------------------------------------------------------
+
+def train(args: argparse.Namespace) -> None:
+    device = torch.device(args.device if torch.cuda.is_available() or args.device == "cpu" else "cpu")
+    if args.device == "cuda" and not torch.cuda.is_available():
+        print("[warn] CUDA not available - falling back to CPU.")
+
+    print(f"[config] device={device}  episodes={args.episodes}  batch={args.update_every} eps  "
+          f"hidden={args.hidden_sizes}  frames={args.history_length}")
+    print(f"[config] curriculum: {args.difficulty_schedule}")
+    print(f"[config] PPO clip_eps={args.clip_eps}  entropy={args.entropy_coef}  lr={args.learning_rate}\n")
+
+    encoder = ObservationEncoder(mode=args.observation_mode)
+    input_dim = encoder.base_dim * args.history_length
+
+    hidden_sizes = tuple(int(h) for h in args.hidden_sizes.split(","))
+    network = ActorCritic(input_dim=input_dim, action_dim=ACTION_DIM, hidden_sizes=hidden_sizes).to(device)
+    optimizer = Adam(network.parameters(), lr=args.learning_rate, eps=1e-5)
+
+    total_steps_for_scheduler = args.episodes // args.update_every
+    scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=args.lr_end_factor,
+                          total_iters=max(1, total_steps_for_scheduler)) if args.lr_decay else None
+
+    env = PyreEnvironment(max_steps=args.max_steps)
+
+    # Build curriculum — patience-gated (dynamic) or static
+    stages = [s.strip().lower() for s in args.difficulty_schedule.split(",") if s.strip()]
+    if args.patience_threshold > 0:
+        patience_curriculum = PatienceCurriculum(
+            stages=stages,
+            threshold=args.patience_threshold,
+            patience_window=args.patience_window,
+            mix_ratio=args.hard_mix_ratio,
+        )
+        static_curriculum: Optional[List[str]] = None
+        print(f"[curriculum] patience-gated: threshold={args.patience_threshold}  "
+              f"window={args.patience_window}  mix={args.hard_mix_ratio}")
+    else:
+        patience_curriculum = None
+        static_curriculum = build_curriculum(args.difficulty_schedule, args.episodes)
+        print(f"[curriculum] static: {args.difficulty_schedule}")
+
+    start_episode = 0
+    if args.resume:
+        resume_path = Path(args.resume)
+        if resume_path.exists():
+            start_episode = load_checkpoint(resume_path, network, optimizer, scheduler)
+
+    # Tracking
+    buffer = RolloutBuffer()
+    episode_rows: List[Dict] = []
+    eval_rows: List[Dict] = []
+    reward_window: deque = deque(maxlen=30)
+    success_window: deque = deque(maxlen=30)
+
+    n_params = sum(p.numel() for p in network.parameters())
+    print(f"[network] Parameters: {n_params:,}")
+    print(f"[network] Input dim:  {input_dim:,}  (encoder.base_dim={encoder.base_dim} x {args.history_length} frames)")
+    print(f"[network] Action dim: {ACTION_DIM}  (4 move + 4 look + 1 wait + {MAX_DOORS} open + {MAX_DOORS} close)")
+    print()
+
+    t_start = time.time()
+
+    for ep_idx in range(start_episode, args.episodes):
+        # Determine difficulty for this episode
+        if patience_curriculum is not None:
+            difficulty = patience_curriculum.current
+        else:
+            difficulty = static_curriculum[ep_idx]  # type: ignore[index]
+
+        result = run_episode(
+            env=env, network=network, encoder=encoder, device=device,
+            difficulty=difficulty, history_length=args.history_length,
+            buffer=buffer, deterministic=False,
+        )
+
+        reward_window.append(result.total_reward)
+        success_window.append(float(result.evacuated))
+
+        # Advance patience curriculum *after* updating success_window
+        if patience_curriculum is not None:
+            difficulty = patience_curriculum.step(float(np.mean(success_window)))
+
+        ep_num = ep_idx + 1
+        episode_rows.append({
+            "episode": ep_num,
+            "difficulty": difficulty,
+            "reward": round(result.total_reward, 4),
+            "evacuated": int(result.evacuated),
+            "steps": result.steps,
+            "final_health": round(result.final_health, 2),
+            "reward_mean_30": round(float(np.mean(reward_window)), 4),
+            "success_rate_30": round(float(np.mean(success_window)), 4),
+        })
+
+        elapsed = time.time() - t_start
+        print(
+            f"ep={ep_num:04d} [{difficulty:<6}] "
+            f"steps={result.steps:03d}  "
+            f"reward={result.total_reward:+8.3f}  "
+            f"evac={int(result.evacuated)}  "
+            f"hp={result.final_health:5.1f}  "
+            f"suc30={float(np.mean(success_window)):.2f}  "
+            f"r30={float(np.mean(reward_window)):+7.2f}  "
+            f"t={elapsed:.0f}s"
+        )
+
+        # PPO update every N episodes
+        should_update = (ep_num % args.update_every == 0) or (ep_num == args.episodes)
+        if should_update and len(buffer) > 0:
+            ppo_metrics = ppo_update(
+                network=network, optimizer=optimizer, buffer=buffer, device=device,
+                clip_eps=args.clip_eps, value_clip_eps=args.clip_eps,
+                entropy_coef=args.entropy_coef, value_coef=args.value_coef,
+                n_epochs=args.update_epochs, minibatch_size=args.minibatch_size,
+                gamma=args.gamma, gae_lambda=args.gae_lambda,
+                max_grad_norm=args.max_grad_norm,
+            )
+            if scheduler:
+                scheduler.step()
+            buffer.clear()
+
+            cur_lr = optimizer.param_groups[0]["lr"]
+            print(
+                f"  >> PPO update  samples={len(buffer) if len(buffer) > 0 else 'flushed'}  "
+                f"pi_loss={ppo_metrics['policy_loss']:+.4f}  "
+                f"v_loss={ppo_metrics['value_loss']:.4f}  "
+                f"entropy={ppo_metrics['entropy']:.4f}  "
+                f"kl={ppo_metrics['approx_kl']:.4f}  "
+                f"clip%={ppo_metrics['clip_frac']:.2f}  "
+                f"lr={cur_lr:.2e}"
+            )
+
+        # Periodic evaluation
+        if args.eval_every > 0 and (ep_num % args.eval_every == 0 or ep_num == args.episodes):
+            eval_m = evaluate_policy(
+                env=env, network=network, encoder=encoder, device=device,
+                difficulty=args.eval_difficulty, history_length=args.history_length,
+                n_episodes=args.eval_episodes,
+            )
+            eval_rows.append({"episode": ep_num, "difficulty": args.eval_difficulty, **{k: round(v, 4) for k, v in eval_m.items()}})
+            print(
+                f"  ** EVAL [{args.eval_difficulty}]  "
+                f"reward={eval_m['reward_mean']:+.3f}  "
+                f"success={eval_m['success_rate']:.2f}  "
+                f"steps={eval_m['steps_mean']:.1f}"
+            )
+
+        # Periodic checkpoint
+        if args.checkpoint and args.checkpoint_every > 0 and ep_num % args.checkpoint_every == 0:
+            save_checkpoint(Path(args.checkpoint), network, optimizer, scheduler, ep_num, args)
+            print(f"  [ckpt] saved -> {args.checkpoint}")
+
+    # Final save
+    if args.output:
+        out = Path(args.output)
+        save_checkpoint(out, network, optimizer, scheduler, args.episodes, args)
+        print(f"\n[done] Model saved -> {out}")
+
+        if args.save_metrics:
+            csv_path = out.with_suffix(".csv")
+            save_csv(csv_path, episode_rows)
+            print(f"[done] Metrics CSV  -> {csv_path}")
+
+        if eval_rows:
+            eval_csv = out.parent / (out.stem + "_eval.csv")
+            save_csv(eval_csv, eval_rows)
+            print(f"[done] Eval CSV     -> {eval_csv}")
+
+        if args.save_graph:
+            png_path = out.with_suffix(".png")
+            save_training_graph_png(png_path, episode_rows, eval_rows)
+            print(f"[done] Graph PNG    -> {png_path}")
+
+    total_time = time.time() - t_start
+    print(f"\n[summary] {args.episodes - start_episode} episodes in {total_time:.1f}s  "
+          f"({(args.episodes - start_episode) / max(1, total_time):.1f} eps/s)")
+    print(f"[summary] Final success rate (last 30): {float(np.mean(success_window)):.2f}")
+    print(f"[summary] Final reward mean  (last 30): {float(np.mean(reward_window)):+.3f}")
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+def describe_env() -> None:
+    print(__doc__)
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(
+        description="PPO training for Pyre fire-evacuation environment",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    # Training scale
+    p.add_argument("--episodes", type=int, default=400, help="Total training episodes")
+    p.add_argument("--max-steps", type=int, default=150, help="Max steps per episode")
+    p.add_argument("--device", type=str, default="cuda", choices=("cuda", "cpu"), help="Torch device")
+
+    # Curriculum
+    p.add_argument("--difficulty", type=str, default="easy", choices=DIFFICULTIES,
+                   help="Single difficulty (overridden by --difficulty-schedule if set)")
+    p.add_argument("--difficulty-schedule", type=str, default="easy,medium,hard",
+                   help="Comma-separated curriculum stages. With --patience-threshold>0 these "
+                        "become gated stages; otherwise split evenly across episodes.")
+    p.add_argument("--patience-threshold", type=float, default=0.65,
+                   help="Success-rate threshold (30-ep window) required before advancing to next "
+                        "difficulty. Set 0 to use static even-split schedule.")
+    p.add_argument("--patience-window", type=int, default=15,
+                   help="Episodes that must sustain >= patience-threshold before advancing.")
+    p.add_argument("--hard-mix-ratio", type=float, default=0.25,
+                   help="Fraction of hard-phase episodes to replay on medium (0=pure hard). "
+                        "Prevents catastrophic forgetting of the medium policy.")
+    p.add_argument("--eval-difficulty", type=str, default="medium", choices=DIFFICULTIES)
+    p.add_argument("--eval-episodes", type=int, default=10)
+    p.add_argument("--eval-every", type=int, default=50)
+
+    # Observation
+    p.add_argument("--observation-mode", type=str, default="visible", choices=("visible", "full"),
+                   help="'visible': partial obs (realistic); 'full': oracle grid (debug)")
+    p.add_argument("--history-length", type=int, default=4,
+                   help="Frames stacked per observation (temporal context for partial obs)")
+
+    # Network
+    p.add_argument("--hidden-sizes", type=str, default="512,256,128",
+                   help="Comma-separated MLP hidden layer sizes")
+
+    # PPO hyperparameters
+    p.add_argument("--update-every", type=int, default=5,
+                   help="Episodes between PPO updates (smaller = faster feedback loop early in training)")
+    p.add_argument("--update-epochs", type=int, default=4,
+                   help="Gradient passes over each collected batch (PPO allows >1)")
+    p.add_argument("--minibatch-size", type=int, default=256)
+    p.add_argument("--clip-eps", type=float, default=0.2, help="PPO surrogate clip ε")
+    p.add_argument("--entropy-coef", type=float, default=0.03,
+                   help="Entropy bonus coefficient — higher = more exploration (0.03 default encourages early exit-seeking)")
+    p.add_argument("--value-coef", type=float, default=0.5)
+    p.add_argument("--gamma", type=float, default=0.99)
+    p.add_argument("--gae-lambda", type=float, default=0.95)
+    p.add_argument("--max-grad-norm", type=float, default=0.5)
+
+    # Optimizer / LR schedule
+    p.add_argument("--learning-rate", type=float, default=3e-4)
+    p.add_argument("--lr-decay", action="store_true", default=True,
+                   help="Linear LR decay to lr_end_factor × initial_lr over training")
+    p.add_argument("--lr-end-factor", type=float, default=0.1,
+                   help="LR at end of training = initial_lr × this value")
+
+    # Persistence
+    p.add_argument("--output", type=str, default="artifacts/pyre_ppo.pt",
+                   help="Path to save final model checkpoint")
+    p.add_argument("--checkpoint", type=str, default="artifacts/pyre_ppo_checkpoint.pt",
+                   help="Path for periodic checkpoints (also used by --resume)")
+    p.add_argument("--checkpoint-every", type=int, default=50)
+    p.add_argument("--resume", type=str, default=None,
+                   help="Path to checkpoint to resume training from")
+    p.add_argument("--save-metrics", action="store_true", default=True,
+                   help="Save per-episode metrics as CSV alongside the model")
+    p.add_argument("--save-graph", action="store_true", default=True,
+                   help="Save a PNG training graph alongside the model (requires matplotlib)")
+
+    # Misc
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--describe-only", action="store_true",
+                   help="Print environment/algorithm description and exit")
+
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    if args.describe_only:
+        describe_env()
+        return
+
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+
+    train(args)
+
+
+if __name__ == "__main__":
+    main()

examples/train_torch_ppo_http.py

@@ -0,0 +1,492 @@
+"""PPO trainer that talks to the Pyre env via HTTP (localhost:8000).
+
+Identical training logic to train_torch_ppo.py, but the environment is
+accessed through the REST API instead of a direct Python import.  This
+lets you run the server once and connect any number of training scripts,
+remote notebooks, or evaluation tools to the same live instance.
+
+Usage
+-----
+1.  Start the server (in a separate terminal):
+        cd openenv-pyre
+        .venv/Scripts/python.exe server/app.py
+
+2.  Run this script:
+        .venv/Scripts/python.exe examples/train_torch_ppo_http.py
+
+Optional flags (identical to train_torch_ppo.py):
+    --server          Base URL of the Pyre server  [default: http://localhost:8000]
+    --episodes        Total training episodes       [default: 400]
+    --difficulty-schedule  Curriculum              [default: easy,easy,easy,medium,medium]
+    --output          Where to save the model .pt  [default: artifacts/pyre_ppo_http.pt]
+    ...               (all other flags are the same as train_torch_ppo.py)
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import os
+import sys
+import time
+from collections import deque
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+import requests
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions import Categorical
+
+# ---------------------------------------------------------------------------
+# Resolve project root so we can import shared models regardless of CWD
+# ---------------------------------------------------------------------------
+_HERE = Path(__file__).resolve().parent
+_ROOT = _HERE.parent
+if str(_ROOT) not in sys.path:
+    sys.path.insert(0, str(_ROOT))
+
+try:
+    from models import PyreAction, PyreMapState, PyreObservation
+except ImportError:
+    from openenv_pyre.models import PyreAction, PyreMapState, PyreObservation
+
+# Reuse all shared utilities from the direct-import trainer
+from examples.train_torch_ppo import (
+    ACTION_KEYS,
+    ACTION_DIM,
+    ACTION_TO_INDEX,
+    DIFFICULTIES,
+    MAX_DOORS,
+    MAX_GRID_H,
+    MAX_GRID_W,
+    WAIT_KEY,
+    WINDS,
+    ActorCritic,
+    ObservationEncoder,
+    RolloutBuffer,
+    action_index_to_env_action,
+    build_action_mask,
+    compute_gae,
+    ppo_update,
+    save_training_graph_png,
+)
+
+
+# ---------------------------------------------------------------------------
+# HTTP environment wrapper
+# ---------------------------------------------------------------------------
+
+class HttpPyreEnv:
+    """Thin wrapper around the Pyre REST API.
+
+    Exposes the same ``reset()`` / ``step()`` interface as ``PyreEnvironment``
+    so the episode runner needs no changes.
+
+    POST /reset  → {"difficulty": str, "seed"?: int}
+    POST /step   → {"action": str, "direction"?: str,
+                    "target_id"?: str, "door_state"?: str}
+    Both return  → {"observation": {...}, "reward": float,
+                    "done": bool, "metadata": {...}}
+    """
+
+    def __init__(self, base_url: str = "http://localhost:8000", timeout: int = 15):
+        self.base_url = base_url.rstrip("/")
+        self.timeout = timeout
+        self.session = requests.Session()
+        self.session.headers.update({"Content-Type": "application/json"})
+
+    # ------------------------------------------------------------------
+    def _parse(self, data: Dict[str, Any]) -> PyreObservation:
+        """Convert a raw JSON response dict into a PyreObservation."""
+        obs_raw = data.get("observation", data)
+
+        map_state: Optional[PyreMapState] = None
+        ms_raw = obs_raw.get("map_state")
+        if ms_raw:
+            map_state = PyreMapState(**ms_raw)
+
+        return PyreObservation(
+            narrative=obs_raw.get("narrative", ""),
+            agent_evacuated=obs_raw.get("agent_evacuated", False),
+            location_label=obs_raw.get("location_label", ""),
+            smoke_level=obs_raw.get("smoke_level", "none"),
+            fire_visible=obs_raw.get("fire_visible", False),
+            fire_direction=obs_raw.get("fire_direction"),
+            agent_health=float(obs_raw.get("agent_health", 100.0)),
+            health_status=obs_raw.get("health_status", "Good"),
+            wind_dir=obs_raw.get("wind_dir", "CALM"),
+            visible_objects=obs_raw.get("visible_objects", []),
+            blocked_exit_ids=obs_raw.get("blocked_exit_ids", []),
+            audible_signals=obs_raw.get("audible_signals", []),
+            elapsed_steps=obs_raw.get("elapsed_steps", 0),
+            last_action_feedback=obs_raw.get("last_action_feedback", ""),
+            available_actions_hint=obs_raw.get("available_actions_hint", []),
+            map_state=map_state,
+            reward=float(data.get("reward", 0.0)),
+            done=bool(data.get("done", False)),
+            metadata=data.get("metadata", {}),
+        )
+
+    # ------------------------------------------------------------------
+    def reset(self, difficulty: str = "easy", seed: Optional[int] = None) -> PyreObservation:
+        payload: Dict[str, Any] = {"difficulty": difficulty}
+        if seed is not None:
+            payload["seed"] = seed
+        resp = self.session.post(
+            f"{self.base_url}/reset", json=payload, timeout=self.timeout
+        )
+        resp.raise_for_status()
+        return self._parse(resp.json())
+
+    # ------------------------------------------------------------------
+    def step(self, action: PyreAction) -> PyreObservation:
+        payload: Dict[str, Any] = {"action": action.action}
+        if action.direction is not None:
+            payload["direction"] = action.direction
+        if action.target_id is not None:
+            payload["target_id"] = action.target_id
+        if action.door_state is not None:
+            payload["door_state"] = action.door_state
+        resp = self.session.post(
+            f"{self.base_url}/step", json=payload, timeout=self.timeout
+        )
+        resp.raise_for_status()
+        return self._parse(resp.json())
+
+    # ------------------------------------------------------------------
+    def health_check(self) -> bool:
+        """Return True if the server is reachable."""
+        try:
+            r = self.session.get(f"{self.base_url}/state", timeout=5)
+            return r.status_code < 500
+        except requests.exceptions.RequestException:
+            return False
+
+
+# ---------------------------------------------------------------------------
+# Episode runner (identical reward shaping as train_torch_ppo.py)
+# ---------------------------------------------------------------------------
+
+@dataclass
+class EpisodeResult:
+    total_reward: float
+    steps: int
+    evacuated: bool
+    final_health: float
+    difficulty: str
+
+
+def run_episode(
+    env: HttpPyreEnv,
+    network: ActorCritic,
+    encoder: ObservationEncoder,
+    device: torch.device,
+    difficulty: str,
+    history_length: int,
+    buffer: RolloutBuffer,
+    deterministic: bool = False,
+) -> EpisodeResult:
+    observation = env.reset(difficulty=difficulty)
+    zero_frame = np.zeros(encoder.base_dim, dtype=np.float32)
+    frames: deque = deque([zero_frame.copy() for _ in range(history_length)], maxlen=history_length)
+    frames.append(encoder.encode(observation))
+
+    total_reward = 0.0
+    final_health = observation.agent_health
+    evacuated = False
+    steps = 0
+    LOOP_WINDOW = 12
+    recent_positions: deque = deque(maxlen=LOOP_WINDOW)
+
+    network.eval()
+    with torch.no_grad():
+        while True:
+            state_vec = np.concatenate(list(frames), dtype=np.float32)
+            action_mask = build_action_mask(observation, exclude_look=True)
+
+            obs_t = torch.tensor(state_vec, dtype=torch.float32, device=device).unsqueeze(0)
+            mask_t = torch.tensor(action_mask, dtype=torch.float32, device=device).unsqueeze(0)
+
+            action_t, log_prob_t, value_t = network.act(obs_t, mask_t, deterministic=deterministic)
+
+            action_idx = int(action_t.item())
+            env_action = action_index_to_env_action(action_idx)
+            next_obs = env.step(env_action)
+
+            reward = float(next_obs.reward or 0.0)
+            chosen_action = env_action.action
+
+            # Shaping 1 — idle penalty
+            if chosen_action == "wait":
+                reward -= 0.05
+
+            # Shaping 2 — fire-approach penalty
+            ms_next = next_obs.map_state
+            if ms_next is not None and chosen_action.startswith("move"):
+                ax, ay = ms_next.agent_x, ms_next.agent_y
+                gw, gh = ms_next.grid_w, ms_next.grid_h
+                for dx, dy in ((0, 1), (0, -1), (1, 0), (-1, 0)):
+                    nx, ny = ax + dx, ay + dy
+                    if 0 <= nx < gw and 0 <= ny < gh:
+                        if float(ms_next.fire_grid[ny * gw + nx]) > 0.15:
+                            reward -= 0.15
+                            break
+
+            # Shaping 3 — anti-loop penalty
+            if ms_next is not None and chosen_action.startswith("move"):
+                cur_pos = (ms_next.agent_x, ms_next.agent_y)
+                if cur_pos in recent_positions:
+                    reward -= 0.2
+                recent_positions.append(cur_pos)
+
+            done = bool(next_obs.done)
+
+            buffer.obs.append(state_vec)
+            buffer.masks.append(action_mask)
+            buffer.actions.append(action_idx)
+            buffer.rewards.append(reward)
+            buffer.log_probs.append(float(log_prob_t.item()))
+            buffer.values.append(float(value_t.item()))
+            buffer.dones.append(done)
+
+            total_reward += reward
+            steps += 1
+            final_health = next_obs.agent_health
+            evacuated = next_obs.agent_evacuated
+
+            frames.append(encoder.encode(next_obs))
+            observation = next_obs
+            if done:
+                break
+
+    return EpisodeResult(
+        total_reward=total_reward,
+        steps=steps,
+        evacuated=evacuated,
+        final_health=final_health,
+        difficulty=difficulty,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Training loop
+# ---------------------------------------------------------------------------
+
+def train(args: argparse.Namespace) -> None:
+    device = torch.device("cuda" if args.device == "cuda" and torch.cuda.is_available() else "cpu")
+    encoder = ObservationEncoder(mode=args.observation_mode)
+    input_dim = encoder.base_dim * args.history_length
+    hidden_sizes = [int(x) for x in args.hidden_sizes.split(",")]
+    action_dim = ACTION_DIM
+
+    # Connect to server
+    env = HttpPyreEnv(base_url=args.server)
+    print(f"[server] Connecting to {args.server} ...", end=" ", flush=True)
+    if not env.health_check():
+        print("FAILED\n[error] Server not reachable. Start it with: python server/app.py")
+        sys.exit(1)
+    print("OK")
+
+    # Network
+    network = ActorCritic(input_dim, action_dim, hidden_sizes).to(device)
+    optimizer = optim.Adam(network.parameters(), lr=args.lr)
+
+    total_params = sum(p.numel() for p in network.parameters())
+    print(f"\n[config] server={args.server}")
+    print(f"[config] device={device}  episodes={args.episodes}  batch={args.update_every} eps")
+    print(f"[config] curriculum: {args.difficulty_schedule}")
+    print(f"[config] PPO clip_eps={args.clip_eps}  entropy={args.entropy_coef}  lr={args.lr}")
+    print(f"\n[network] Parameters: {total_params:,}")
+    print(f"[network] Input dim:  {input_dim:,}  (encoder.base_dim={encoder.base_dim} x {args.history_length} frames)")
+    print(f"[network] Action dim: {action_dim}  (4 move + 4 look + 1 wait + {MAX_DOORS} open + {MAX_DOORS} close)\n", flush=True)
+
+    schedule = args.difficulty_schedule.split(",")
+    buffer = RolloutBuffer()
+    metrics: list = []
+    eval_metrics: list = []
+    success_window: deque = deque(maxlen=30)
+    reward_window: deque = deque(maxlen=30)
+    t0 = time.time()
+    lr_scheduler = optim.lr_scheduler.LinearLR(
+        optimizer, start_factor=1.0, end_factor=0.1, total_iters=args.episodes
+    )
+
+    for ep in range(1, args.episodes + 1):
+        stage_idx = min(int((ep - 1) / args.episodes * len(schedule)), len(schedule) - 1)
+        difficulty = schedule[stage_idx]
+
+        result = run_episode(env, network, encoder, device, difficulty, args.history_length, buffer)
+        success_window.append(1 if result.evacuated else 0)
+        reward_window.append(result.total_reward)
+        suc30 = sum(success_window) / len(success_window)
+        r30 = sum(reward_window) / len(reward_window)
+        elapsed = int(time.time() - t0)
+
+        evac_sym = "1" if result.evacuated else "0"
+        print(
+            f"ep={ep:04d} [{difficulty:<6}] steps={result.steps:03d}  "
+            f"reward={result.total_reward:+8.3f}  evac={evac_sym}  "
+            f"hp={result.final_health:5.1f}  suc30={suc30:.2f}  "
+            f"r30={r30:+7.2f}  t={elapsed}s"
+        )
+
+        metrics.append({
+            "episode": ep, "difficulty": difficulty, "steps": result.steps,
+            "reward": round(result.total_reward, 4), "evacuated": int(result.evacuated),
+            "final_health": result.final_health, "suc30": round(suc30, 3), "r30": round(r30, 3),
+        })
+
+        # PPO update
+        if ep % args.update_every == 0 and len(buffer.obs) > 0:
+            network.train()
+            stats = ppo_update(
+                network=network, optimizer=optimizer, buffer=buffer, device=device,
+                clip_eps=args.clip_eps, value_clip_eps=args.clip_eps,
+                entropy_coef=args.entropy_coef, value_coef=args.value_coef,
+                n_epochs=args.update_epochs, minibatch_size=args.minibatch_size,
+                gamma=args.gamma, gae_lambda=args.gae_lambda,
+                max_grad_norm=args.max_grad_norm,
+            )
+            lr_scheduler.step()
+            cur_lr = optimizer.param_groups[0]["lr"]
+            print(
+                f"  >> PPO update  samples=flushed  "
+                f"pi_loss={stats['policy_loss']:+.4f}  v_loss={stats['value_loss']:.4f}  "
+                f"entropy={stats['entropy']:.4f}  kl={stats['approx_kl']:.4f}  "
+                f"clip%={stats['clip_frac']:.2f}  lr={cur_lr:.2e}"
+            )
+            buffer.clear()
+            network.eval()
+
+        # Evaluation
+        if ep % args.eval_every == 0:
+            eval_rewards, eval_success, eval_steps_list = [], [], []
+            eval_buf = RolloutBuffer()
+            for _ in range(args.eval_episodes):
+                er = run_episode(
+                    env, network, encoder, device,
+                    args.eval_difficulty, args.history_length,
+                    eval_buf, deterministic=True,
+                )
+                eval_rewards.append(er.total_reward)
+                eval_success.append(1 if er.evacuated else 0)
+                eval_steps_list.append(er.steps)
+            avg_r = sum(eval_rewards) / len(eval_rewards)
+            avg_s = sum(eval_success) / len(eval_success)
+            avg_st = sum(eval_steps_list) / len(eval_steps_list)
+            print(f"  ** EVAL [{args.eval_difficulty}]  reward={avg_r:+.3f}  success={avg_s:.2f}  steps={avg_st:.1f}")
+            eval_metrics.append({
+                "episode": ep, "eval_difficulty": args.eval_difficulty,
+                "avg_reward": round(avg_r, 4), "success_rate": round(avg_s, 3),
+                "avg_steps": round(avg_st, 1),
+            })
+
+        # Checkpoint
+        if args.checkpoint and ep % args.checkpoint_every == 0:
+            torch.save(network.state_dict(), args.checkpoint)
+            print(f"  [ckpt] saved -> {args.checkpoint}")
+
+    # --- Save artefacts ---
+    out = Path(args.output)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    torch.save(network.state_dict(), out)
+    print(f"\n[done] Model saved -> {out}")
+
+    if args.save_metrics and metrics:
+        csv_path = out.with_suffix(".csv")
+        with open(csv_path, "w", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=metrics[0].keys())
+            writer.writeheader()
+            writer.writerows(metrics)
+        print(f"[done] Metrics CSV  -> {csv_path}")
+
+        if eval_metrics:
+            eval_csv = out.with_stem(out.stem + "_eval").with_suffix(".csv")
+            with open(eval_csv, "w", newline="") as f:
+                writer = csv.DictWriter(f, fieldnames=eval_metrics[0].keys())
+                writer.writeheader()
+                writer.writerows(eval_metrics)
+            print(f"[done] Eval CSV     -> {eval_csv}")
+
+    if args.save_graph:
+        try:
+            png_path = out.with_suffix(".png")
+            save_training_graph_png(metrics, eval_metrics, str(png_path))
+            print(f"[done] Graph PNG    -> {png_path}")
+        except Exception as e:
+            print(f"[warn] Graph skipped: {e}")
+
+    suc_final = sum(success_window) / max(1, len(success_window))
+    r_final = sum(reward_window) / max(1, len(reward_window))
+    elapsed_total = time.time() - t0
+    print(f"\n[summary] {args.episodes} episodes in {elapsed_total:.1f}s  ({args.episodes / elapsed_total:.1f} eps/s)")
+    print(f"[summary] Final success rate (last 30): {suc_final:.2f}")
+    print(f"[summary] Final reward mean  (last 30): {r_final:+.3f}")
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(
+        description="PPO trainer using the Pyre HTTP server (localhost:8000)"
+    )
+
+    # Server
+    p.add_argument("--server", type=str, default="http://localhost:8000",
+                   help="Base URL of the running Pyre env server")
+
+    # Training
+    p.add_argument("--episodes", type=int, default=400)
+    p.add_argument("--device", type=str, default="cpu", choices=("cuda", "cpu"))
+
+    # Curriculum
+    p.add_argument("--difficulty-schedule", type=str, default="easy,easy,easy,medium,medium")
+    p.add_argument("--eval-difficulty", type=str, default="medium", choices=DIFFICULTIES)
+    p.add_argument("--eval-episodes", type=int, default=10)
+    p.add_argument("--eval-every", type=int, default=50)
+
+    # Observation
+    p.add_argument("--observation-mode", type=str, default="visible", choices=("visible", "full"))
+    p.add_argument("--history-length", type=int, default=4)
+
+    # Network
+    p.add_argument("--hidden-sizes", type=str, default="256,128,64")
+
+    # PPO
+    p.add_argument("--lr", type=float, default=3e-4)
+    p.add_argument("--gamma", type=float, default=0.99)
+    p.add_argument("--gae-lambda", type=float, default=0.95)
+    p.add_argument("--clip-eps", type=float, default=0.2)
+    p.add_argument("--value-coef", type=float, default=0.5)
+    p.add_argument("--entropy-coef", type=float, default=0.03)
+    p.add_argument("--update-every", type=int, default=5)
+    p.add_argument("--update-epochs", type=int, default=4)
+    p.add_argument("--minibatch-size", type=int, default=256)
+    p.add_argument("--max-grad-norm", type=float, default=0.5)
+
+    # Output
+    p.add_argument("--output", type=str, default="artifacts/pyre_ppo_http.pt")
+    p.add_argument("--checkpoint", type=str, default="artifacts/pyre_ppo_http_ckpt.pt")
+    p.add_argument("--checkpoint-every", type=int, default=50)
+    p.add_argument("--save-metrics", action="store_true", default=True)
+    p.add_argument("--save-graph", action="store_true", default=True)
+    p.add_argument("--seed", type=int, default=42)
+
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    train(args)
+
+
+if __name__ == "__main__":
+    main()

frontend/README.md

@@ -0,0 +1,93 @@
+# Pyre — Frontend Visualization
+
+A cinematic real-time visualization for the **Pyre Crisis Navigation Environment** — a reinforcement learning environment where an LLM agent navigates a burning building.
+
+## Quick start
+
+```bash
+# Open directly in a browser — no build step needed
+open frontend/index.html
+```
+
+The app runs entirely in-browser. **Demo mode** simulates the fire physics in JavaScript (no server required). **Live mode** connects to the deployed environment.
+
+---
+
+## Demo mode vs Live mode
+
+| | Demo | Live |
+|---|---|---|
+| Server needed | ✗ | ✓ |
+| Fire physics | JS port (exact match) | Python server |
+| Full reward rubric | Simplified | Complete |
+| Toggle | Default | Click "Live" in topbar |
+
+**Live server:** `https://krooz-pyre-env.hf.space`
+
+---
+
+## Controls
+
+| Key | Action |
+|---|---|
+| `Space` | Play / pause |
+| `→` | Single step |
+| `R` | New episode |
+| `1`–`5` | Speed ½× / 1× / 2× / 4× / 8× |
+
+Bottom bar: difficulty selector, seed input, speed control, reset.
+
+---
+
+## Recording episodes (Python)
+
+```bash
+pip install requests  # only stdlib used, no install needed
+
+python bridge/recorder.py \
+  --url https://krooz-pyre-env.hf.space/web \
+  --episodes 10 \
+  --difficulty medium \
+  --out episodes/
+```
+
+Episodes are saved as JSON files to `episodes/`. Each file contains full frame-by-frame grid data (cell, fire, smoke grids + agent position + visible cells).
+
+---
+
+## File structure
+
+```
+frontend/
+├── index.html       Main app — open this
+└── js/
+    ├── sim.js       JS port of pyre_env fire simulation + floor plans
+    ├── renderer.js  Canvas2D rendering (fire particles, fog-of-war, agent trail)
+    └── app.js       App controller, charts, HUD, live/demo modes
+
+bridge/
+└── recorder.py      Record live episodes to JSON for replay
+```
+
+---
+
+## Architecture notes
+
+**Rendering:** HTML5 Canvas 2D — sufficient at 60fps for 16×16 grids; additive blending (`globalCompositeOperation: lighter`) for fire glow; ember particle pool (200 max); fog-of-war via per-cell alpha overlay.
+
+**Demo agent:** BFS toward nearest unblocked exit, 15% random exploration, avoids fire cells > 0.4 intensity.
+
+**Live bridge:** Polls `/web/scene` every 800ms; applies grid state to the same rendering pipeline.
+
+---
+
+## Demo script (30-second stage walkthrough)
+
+1. **Open** `frontend/index.html` — fire simulation starts automatically at 1×
+2. **Point out** the dark floor plan canvas with glowing fire cells, fog-of-war, and cyan agent dot
+3. **Slow to ½×** to show per-step fire propagation and smoke spread
+4. **Speed to 4×** — show agent navigating toward exits (green glow), closing doors (blue bars) to slow fire
+5. **Highlight** the side panel: cumulative reward curve dipping on smoke exposure, fire cell count climbing, action histogram
+6. **Describe partial observability** — the dark unexplored cells vs. visible corridor
+7. **Reset (R)** with a different seed to show episode variety
+8. If server is available: click **Live** — "Connected" chip turns green, real Python environment takes over

frontend/eslint.config.js

@@ -0,0 +1,22 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import tseslint from 'typescript-eslint'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      js.configs.recommended,
+      tseslint.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      globals: globals.browser,
+    },
+  },
+])

frontend/index.html

@@ -0,0 +1,16 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Pyre — Crisis Navigation</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com" />
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+    <link href="https://fonts.googleapis.com/css2?family=DM+Mono:wght@400;500&family=DM+Sans:wght@400;500;600&display=swap" rel="stylesheet" />
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>

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+    "node_modules/yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/zod": {
+      "version": "4.3.6",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.3.6.tgz",
+      "integrity": "sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==",
+      "dev": true,
+      "license": "MIT",
+      "funding": {
+        "url": "https://github.com/sponsors/colinhacks"
+      }
+    },
+    "node_modules/zod-validation-error": {
+      "version": "4.0.2",
+      "resolved": "https://registry.npmjs.org/zod-validation-error/-/zod-validation-error-4.0.2.tgz",
+      "integrity": "sha512-Q6/nZLe6jxuU80qb/4uJ4t5v2VEZ44lzQjPDhYJNztRQ4wyWc6VF3D3Kb/fAuPetZQnhS3hnajCf9CsWesghLQ==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=18.0.0"
+      },
+      "peerDependencies": {
+        "zod": "^3.25.0 || ^4.0.0"
+      }
+    }
+  }
+}

frontend/package.json

@@ -0,0 +1,30 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc -b && vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^19.2.5",
+    "react-dom": "^19.2.5"
+  },
+  "devDependencies": {
+    "@eslint/js": "^10.0.1",
+    "@types/node": "^24.12.2",
+    "@types/react": "^19.2.14",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^6.0.1",
+    "eslint": "^10.2.1",
+    "eslint-plugin-react-hooks": "^7.1.1",
+    "eslint-plugin-react-refresh": "^0.5.2",
+    "globals": "^17.5.0",
+    "typescript": "~6.0.2",
+    "typescript-eslint": "^8.58.2",
+    "vite": "^8.0.10"
+  }
+}

frontend/src/App.css

@@ -0,0 +1,703 @@
+/* ── Reset ── */
+*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
+
+/* ── Design Tokens ── */
+:root {
+  --bg:    #f5f2ed;
+  --s1:    #ffffff;
+  --s2:    #faf9f6;
+  --s3:    #f0ede8;
+  --bd:    #e4dfd7;
+  --bd2:   #cdc7be;
+  --t1:    #1c1712;
+  --t2:    #6b6460;
+  --t3:    #a8a29e;
+  --fire:  #c2410c;
+  --fire2: rgba(194,65,12,0.08);
+  --blue:  #1d4ed8;
+  --blue2: rgba(29,78,216,0.08);
+  --green: #166534;
+  --green2:rgba(22,101,52,0.08);
+  --amber: #92400e;
+  --amber2:rgba(146,64,14,0.08);
+  --red:   #b91c1c;
+  --red2:  rgba(185,28,28,0.08);
+  --gold:  #d97706;
+  --gold2: rgba(217,119,6,0.12);
+  --mono: 'DM Mono', 'JetBrains Mono', monospace;
+  --sans: 'DM Sans', 'Inter', system-ui, sans-serif;
+  --r: 8px;
+}
+
+body {
+  background: var(--bg);
+  color: var(--t1);
+  font-family: var(--sans);
+  font-size: 13px;
+  line-height: 1.5;
+  overflow: hidden;
+}
+
+/* ── Shell ── */
+.shell {
+  display: grid;
+  grid-template-rows: 48px 1fr;
+  height: 100vh;
+  overflow: hidden;
+}
+
+/* ── Topbar ── */
+.topbar {
+  background: var(--s1);
+  border-bottom: 1px solid var(--bd);
+  display: flex;
+  align-items: center;
+  padding: 0 20px;
+  gap: 16px;
+  z-index: 20;
+  box-shadow: 0 1px 3px rgba(0,0,0,0.04);
+}
+
+.brand {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.brand-icon {
+  width: 28px;
+  height: 28px;
+  background: linear-gradient(135deg, #c2410c, #f97316);
+  border-radius: 6px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 14px;
+}
+
+.brand-name {
+  font-family: var(--mono);
+  font-size: 15px;
+  font-weight: 500;
+  color: var(--t1);
+  letter-spacing: 0.06em;
+  text-transform: uppercase;
+}
+
+.brand-sep {
+  color: var(--bd2);
+  margin: 0 2px;
+}
+
+.brand-sub {
+  font-size: 11px;
+  color: var(--t3);
+  letter-spacing: 0.08em;
+  font-family: var(--mono);
+}
+
+.topbar-right {
+  margin-left: auto;
+  display: flex;
+  align-items: center;
+  gap: 12px;
+}
+
+.live-chip {
+  display: flex;
+  align-items: center;
+  gap: 5px;
+  font-family: var(--mono);
+  font-size: 10px;
+  font-weight: 500;
+  letter-spacing: 0.08em;
+  text-transform: uppercase;
+  padding: 4px 10px;
+  border-radius: 20px;
+  border: 1px solid var(--bd);
+  color: var(--t3);
+  background: var(--s2);
+}
+
+.live-chip::before {
+  content: '';
+  width: 6px;
+  height: 6px;
+  border-radius: 50%;
+  background: var(--bd2);
+}
+
+.live-chip.online {
+  color: var(--green);
+  background: var(--green2);
+  border-color: rgba(22,101,52,0.2);
+}
+
+.live-chip.online::before { background: #22c55e; animation: blink 1.2s infinite; }
+
+.live-chip.offline {
+  color: var(--t3);
+  background: var(--s2);
+}
+
+.live-chip.error {
+  color: var(--red);
+  background: var(--red2);
+  border-color: rgba(185,28,28,0.2);
+}
+
+.live-chip.error::before { background: var(--red); }
+
+@keyframes blink { 0%,100% { opacity:1 } 50% { opacity:0.3 } }
+
+.topbar-ep {
+  font-family: var(--mono);
+  font-size: 10px;
+  color: var(--t3);
+  display: flex;
+  align-items: center;
+  gap: 6px;
+}
+
+.topbar-ep .ep-id {
+  color: var(--blue);
+  font-weight: 500;
+}
+
+/* ── Content Layout ── */
+.content {
+  display: grid;
+  grid-template-columns: 1fr 300px;
+  overflow: hidden;
+  height: 100%;
+}
+
+/* ── Canvas Zone (left) ── */
+.canvas-zone {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: flex-start;
+  padding: 20px;
+  overflow-y: auto;
+  gap: 16px;
+  background: var(--bg);
+}
+
+.canvas-frame {
+  position: relative;
+  border-radius: 10px;
+  overflow: hidden;
+  box-shadow:
+    0 0 0 1px var(--bd2),
+    0 4px 24px rgba(0,0,0,0.10),
+    0 1px 4px rgba(0,0,0,0.06);
+  background: #d4c9a8;
+  line-height: 0;
+  flex-shrink: 0;
+}
+
+#map-canvas {
+  display: block;
+  max-width: 100%;
+  max-height: calc(100vh - 48px - 120px);
+  image-rendering: pixelated;
+}
+
+/* ── HUD Overlays on Canvas ── */
+.hud-overlay {
+  position: absolute;
+  top: 0; left: 0; right: 0;
+  display: flex;
+  justify-content: space-between;
+  pointer-events: none;
+  z-index: 10;
+  padding: 10px;
+  gap: 8px;
+}
+
+.hud-card {
+  background: rgba(12, 8, 5, 0.82);
+  backdrop-filter: blur(10px);
+  border: 1px solid rgba(255,255,255,0.10);
+  border-radius: 6px;
+  padding: 7px 10px;
+  color: #ede6dc;
+  display: flex;
+  flex-direction: column;
+  gap: 3px;
+  min-width: 130px;
+}
+
+.hud-card-center {
+  min-width: 160px;
+  border-color: rgba(251,191,36,0.18);
+}
+
+.hud-r {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+}
+
+.hlbl {
+  font-family: var(--mono);
+  font-size: 9px;
+  color: rgba(168,162,158,0.7);
+  letter-spacing: 0.10em;
+  text-transform: uppercase;
+  flex-shrink: 0;
+}
+
+.hval {
+  font-family: var(--mono);
+  font-size: 13px;
+  font-weight: 500;
+  color: #f0e8e0;
+  margin-left: auto;
+  min-width: 26px;
+  text-align: right;
+}
+
+.hbar-bg {
+  flex: 1;
+  height: 5px;
+  background: rgba(255,255,255,0.10);
+  border-radius: 3px;
+  overflow: hidden;
+}
+
+.hbar-fill {
+  height: 100%;
+  border-radius: 3px;
+  transition: width 0.35s ease;
+}
+
+.hbar-fill.g { background: linear-gradient(90deg, #22c55e, #4ade80); }
+.hbar-fill.m { background: linear-gradient(90deg, #f59e0b, #fbbf24); }
+.hbar-fill.c { background: linear-gradient(90deg, #ef4444, #f87171); }
+
+.hstatus {
+  font-family: var(--mono);
+  font-size: 9px;
+  font-weight: 500;
+  letter-spacing: 0.06em;
+  text-transform: uppercase;
+  padding: 2px 6px;
+  border-radius: 3px;
+}
+
+.hstatus.good     { color: #4ade80; background: rgba(74,222,128,0.12); }
+.hstatus.moderate { color: #fbbf24; background: rgba(251,191,36,0.12); }
+.hstatus.low      { color: #fb923c; background: rgba(251,146,60,0.12); }
+.hstatus.critical { color: #f87171; background: rgba(248,113,113,0.12); animation: pulse-red 1s infinite; }
+
+@keyframes pulse-red { 0%,100%{opacity:1} 50%{opacity:0.5} }
+
+.step-val {
+  font-family: var(--mono);
+  font-size: 12px;
+  font-weight: 500;
+  color: #f0e8e0;
+}
+
+.sbar-bg {
+  height: 3px;
+  background: rgba(255,255,255,0.10);
+  border-radius: 2px;
+  overflow: hidden;
+  width: 100%;
+  margin-top: 1px;
+}
+
+.sbar-fill {
+  height: 100%;
+  background: linear-gradient(90deg, var(--blue), #60a5fa);
+  border-radius: 2px;
+  transition: width 0.35s ease;
+}
+
+.step-meta {
+  font-family: var(--mono);
+  font-size: 8px;
+  color: rgba(168,162,158,0.55);
+  letter-spacing: 0.06em;
+  text-transform: uppercase;
+  margin-top: 1px;
+}
+
+/* ── Legend below canvas ── */
+.legend {
+  display: flex;
+  gap: 14px;
+  flex-wrap: wrap;
+  align-items: center;
+  padding: 8px 12px;
+  background: var(--s1);
+  border: 1px solid var(--bd);
+  border-radius: 6px;
+  width: 100%;
+  max-width: 640px;
+}
+
+.legend-item {
+  display: flex;
+  align-items: center;
+  gap: 5px;
+  font-family: var(--mono);
+  font-size: 9px;
+  color: var(--t2);
+  letter-spacing: 0.05em;
+  text-transform: uppercase;
+}
+
+.leg-swatch {
+  width: 12px;
+  height: 12px;
+  border-radius: 2px;
+  flex-shrink: 0;
+}
+
+/* ── Dialog / Field Report ── */
+.dialog {
+  background: var(--s1);
+  border: 1px solid var(--bd);
+  border-left: 3px solid var(--fire);
+  border-radius: var(--r);
+  padding: 10px 14px;
+  font-family: var(--mono);
+  font-size: 12px;
+  color: var(--t2);
+  width: 100%;
+  max-width: 640px;
+  line-height: 1.5;
+}
+
+.dialog-who {
+  font-size: 9px;
+  color: var(--t3);
+  text-transform: uppercase;
+  letter-spacing: 0.10em;
+  display: block;
+  margin-bottom: 3px;
+}
+
+/* ── Side Panel (right) ── */
+.side {
+  background: var(--s1);
+  border-left: 1px solid var(--bd);
+  display: flex;
+  flex-direction: column;
+  overflow-y: auto;
+  overflow-x: hidden;
+}
+
+.side-sec {
+  border-bottom: 1px solid var(--bd);
+  padding: 14px 16px;
+}
+
+.sec-hd {
+  font-family: var(--mono);
+  font-size: 9px;
+  font-weight: 600;
+  color: var(--t3);
+  text-transform: uppercase;
+  letter-spacing: 0.12em;
+  margin-bottom: 10px;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+
+/* ── Stat Grid (2×N) ── */
+.sg {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 6px;
+}
+
+.sc {
+  background: var(--s2);
+  border: 1px solid var(--bd);
+  border-radius: 6px;
+  padding: 8px 10px;
+}
+
+.sc-l {
+  font-family: var(--mono);
+  font-size: 9px;
+  color: var(--t3);
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+  margin-bottom: 2px;
+}
+
+.sc-v {
+  font-family: var(--mono);
+  font-size: 14px;
+  font-weight: 500;
+  color: var(--t1);
+}
+
+.sc-v.fire  { color: var(--fire); }
+.sc-v.blue  { color: var(--blue); }
+.sc-v.green { color: var(--green); }
+.sc-v.amber { color: var(--amber); }
+.sc-v.gold  { color: var(--gold); }
+
+/* ── Status Rows ── */
+.srow {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 5px 0;
+  font-size: 12px;
+  border-bottom: 1px solid rgba(0,0,0,0.03);
+}
+
+.srow:last-child { border-bottom: none; }
+
+.sv { font-family: var(--mono); font-weight: 500; }
+.sv.blue  { color: var(--blue); }
+.sv.green { color: var(--green); }
+.sv.hot   { color: var(--fire); }
+.sv.danger{ color: var(--red); }
+.sv.warn  { color: var(--amber); }
+.sv.gold  { color: var(--gold); }
+
+/* ── Health bar in side panel ── */
+.bar-w { margin-top: 10px; }
+
+.bar-lbl {
+  font-family: var(--mono);
+  font-size: 9px;
+  color: var(--t3);
+  display: flex;
+  justify-content: space-between;
+  margin-bottom: 5px;
+  text-transform: uppercase;
+  letter-spacing: 0.06em;
+}
+
+.bar-bg {
+  background: var(--s2);
+  border: 1px solid var(--bd);
+  border-radius: 4px;
+  height: 6px;
+  overflow: hidden;
+}
+
+.bar-fill {
+  height: 100%;
+  transition: width 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+  border-radius: 4px;
+}
+
+/* ── Event Log ── */
+.elog {
+  display: flex;
+  flex-direction: column;
+  gap: 2px;
+  max-height: 160px;
+  overflow-y: auto;
+}
+
+.erow {
+  display: flex;
+  align-items: flex-start;
+  gap: 6px;
+  padding: 4px 6px;
+  border-radius: 4px;
+  font-size: 11px;
+  background: transparent;
+}
+
+.erow.alarm {
+  background: var(--red2);
+  border-left: 2px solid var(--red);
+}
+
+.erow.warn-row {
+  background: var(--amber2);
+  border-left: 2px solid var(--amber);
+}
+
+.estep {
+  font-family: var(--mono);
+  font-size: 9px;
+  color: var(--t3);
+  min-width: 26px;
+  padding-top: 1px;
+  flex-shrink: 0;
+}
+
+.etext {
+  color: var(--t2);
+  line-height: 1.4;
+  flex: 1;
+  font-size: 11px;
+}
+
+.erwd {
+  font-family: var(--mono);
+  font-size: 9px;
+  font-weight: 500;
+  flex-shrink: 0;
+  padding-top: 1px;
+}
+
+.erwd.p { color: var(--green); }
+.erwd.n { color: var(--red); }
+
+/* ── Direction Pad ── */
+.dpad {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 5px;
+  max-width: 140px;
+  margin: 0 auto;
+}
+
+.dpad-center {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+/* ── Control Buttons ── */
+.ctrl-grid {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 5px;
+}
+
+.ctrl-row {
+  display: flex;
+  gap: 5px;
+  margin-top: 8px;
+}
+
+.ctrl-btn {
+  background: var(--s2);
+  color: var(--t2);
+  border: 1px solid var(--bd);
+  border-radius: 5px;
+  padding: 7px 6px;
+  font-family: var(--mono);
+  font-size: 10px;
+  font-weight: 500;
+  letter-spacing: 0.04em;
+  cursor: pointer;
+  transition: all 0.1s;
+  text-align: center;
+  flex: 1;
+}
+
+.ctrl-btn:hover:not(:disabled) {
+  background: var(--s3);
+  color: var(--t1);
+  border-color: var(--bd2);
+}
+
+.ctrl-btn:disabled {
+  opacity: 0.4;
+  cursor: not-allowed;
+}
+
+.ctrl-btn.accent {
+  background: var(--fire2);
+  color: var(--fire);
+  border-color: rgba(194,65,12,0.25);
+}
+
+.ctrl-btn.accent:hover {
+  background: rgba(194,65,12,0.15);
+  color: var(--fire);
+}
+
+.ctrl-btn.active {
+  background: var(--blue2);
+  color: var(--blue);
+  border-color: rgba(29,78,216,0.25);
+}
+
+.ctrl-btn.active:hover {
+  background: rgba(29,78,216,0.15);
+}
+
+.ctrl-btn.play {
+  background: linear-gradient(135deg, #c2410c, #f97316);
+  color: white;
+  border: none;
+  font-size: 11px;
+  box-shadow: 0 2px 8px rgba(194,65,12,0.30);
+}
+
+.ctrl-btn.play:hover {
+  box-shadow: 0 3px 12px rgba(194,65,12,0.45);
+  filter: brightness(1.05);
+}
+
+.ctrl-status {
+  margin-top: 10px;
+  font-family: var(--mono);
+  font-size: 10px;
+  color: var(--t3);
+  padding: 7px 10px;
+  background: var(--s2);
+  border-radius: 5px;
+  border: 1px solid var(--bd);
+  border-left: 3px solid var(--bd2);
+}
+
+.ctrl-status.error {
+  color: var(--red);
+  background: var(--red2);
+  border-left-color: var(--red);
+}
+
+/* ── Door List ── */
+.door-grid {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 4px;
+}
+
+.door-btn {
+  background: var(--s2);
+  border: 1px solid var(--bd);
+  border-radius: 5px;
+  padding: 5px 4px;
+  font-family: var(--mono);
+  font-size: 9px;
+  cursor: pointer;
+  color: var(--t2);
+  text-align: center;
+  transition: all 0.1s;
+}
+
+.door-btn:hover { background: var(--s3); color: var(--t1); }
+.door-btn.open { color: var(--green); border-color: rgba(22,101,52,0.25); background: var(--green2); }
+.door-btn.closed { color: var(--amber); border-color: rgba(146,64,14,0.25); background: var(--amber2); }
+.door-btn.failed { color: var(--red); border-color: rgba(185,28,28,0.25); background: var(--red2); }
+
+/* ── API Report ── */
+.report-box {
+  background: var(--s2);
+  border: 1px solid var(--bd);
+  border-radius: 5px;
+  padding: 10px;
+  max-height: 160px;
+  overflow-y: auto;
+  font-family: var(--mono);
+  font-size: 9px;
+  color: var(--t2);
+  white-space: pre-wrap;
+  line-height: 1.5;
+}
+
+/* ── Scrollbar ── */
+::-webkit-scrollbar { width: 4px; height: 4px; }
+::-webkit-scrollbar-track { background: transparent; }
+::-webkit-scrollbar-thumb { background: var(--bd2); border-radius: 2px; }

frontend/src/App.tsx

@@ -0,0 +1,460 @@
+import { useState, useEffect, useCallback, useRef } from 'react';
+import './App.css';
+import type { Observation, Door, ApiReport, SceneResponse } from './types';
+import Map2D from './components/Map2D';
+import HUD from './components/HUD';
+import ControlPanel from './components/ControlPanel';
+import APIReport from './components/APIReport';
+
+const DOOR_CLOSED = 3;
+const OBSTACLE    = 5;
+
+interface EventEntry {
+  step: number;
+  text: string;
+  reward: number;
+  isAlarm?: boolean;
+}
+
+function App() {
+  const [observation,    setObservation]    = useState<Observation | null>(null);
+  const [sceneData,      setSceneData]      = useState<SceneResponse | null>(null);
+  const [isAutoWait,     setIsAutoWait]     = useState(false);
+  const [isPolling,      setIsPolling]      = useState(true);
+  const [status,         setStatus]         = useState('Idle — waiting for connection');
+  const [isError,        setIsError]        = useState(false);
+  const [apiReport,      setApiReport]      = useState<ApiReport | null>(null);
+  const [agentMoveCount, setAgentMoveCount] = useState(0);
+  const [agentMoveFlash, setAgentMoveFlash] = useState(0);
+  const [eventLog,       setEventLog]       = useState<EventEntry[]>([]);
+
+  const prevAgentPos  = useRef({ x: -1, y: -1 });
+  const autoWaitTimer = useRef<number | null>(null);
+  const pollTimer     = useRef<number | null>(null);
+  const logEndRef     = useRef<HTMLDivElement | null>(null);
+
+  /* scroll event log to bottom */
+  useEffect(() => {
+    logEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  }, [eventLog]);
+
+  const setStatusMsg = (msg: string, error = false) => {
+    setStatus(msg);
+    setIsError(error);
+  };
+
+  const pushLog = (text: string, step: number, reward: number, isAlarm = false) => {
+    setEventLog(prev => [...prev.slice(-49), { step, text, reward, isAlarm }]);
+  };
+
+  const applyObservation = useCallback((obs: Observation) => {
+    const newX = obs.map_state.agent_x;
+    const newY = obs.map_state.agent_y;
+    if (prevAgentPos.current.x !== -1 &&
+       (newX !== prevAgentPos.current.x || newY !== prevAgentPos.current.y)) {
+      setAgentMoveFlash(18);
+      setAgentMoveCount(c => c + 1);
+    }
+    prevAgentPos.current = { x: newX, y: newY };
+    setObservation(obs);
+  }, []);
+
+  const updateReport = (kind: string, request: unknown, response: any) => {
+    const mapState = response?.observation?.map_state || response?.map_state || response?.graph;
+    const template = mapState?.template_name || response?.labels?.episode?.template || 'unknown';
+    const step     = mapState?.step_count ?? response?.observation?.elapsed_steps ?? response?.labels?.episode?.step ?? '-';
+    const reward   = Number(response?.reward ?? 0).toFixed(3);
+    const done     = Boolean(response?.done);
+    setApiReport({
+      call_type: kind,
+      request,
+      response,
+      meta: `${kind.toUpperCase()} | template=${template} | step=${step} | reward=${reward} | done=${done}`,
+    });
+  };
+
+  const apiCall = async (path: string, payload: unknown) => {
+    const res = await fetch(path, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(payload || {}),
+    });
+    if (!res.ok) {
+      const text = await res.text();
+      throw new Error(`${res.status} ${res.statusText}: ${text}`);
+    }
+    return res.json();
+  };
+
+  const resetLive = async (difficulty = 'medium') => {
+    try {
+      setStatusMsg('Initiating Reset...');
+      const payload = { difficulty };
+      const data = await apiCall('/reset', payload);
+      const obs: Observation = data.observation;
+      if (data.observation?.map_state) {
+        obs.metadata = {
+          fire_sources:    data.observation.fire_sources_count ?? 0,
+          fire_spread_rate:data.observation.fire_spread_rate   ?? 0,
+          humidity:        data.observation.humidity           ?? 0,
+          difficulty,
+        };
+      }
+      applyObservation(obs);
+      updateReport('reset', payload, data);
+      setStatusMsg(`Ready. Reward: ${Number(data.reward || 0).toFixed(2)}`);
+      pushLog('Episode reset. Assess surroundings.', obs.map_state.step_count, data.reward ?? 0);
+    } catch (err: any) {
+      setStatusMsg(`Reset Failed: ${err.message}`, true);
+    }
+  };
+
+  const resetUntilDoors = async () => {
+    try {
+      setStatusMsg('Searching for layout with doors...');
+      for (let i = 1; i <= 8; i++) {
+        const payload = { difficulty: 'medium' };
+        const data    = await apiCall('/reset', payload);
+        const doorCount = Object.keys(data?.observation?.map_state?.door_registry || {}).length;
+        if (doorCount > 0) {
+          const obs: Observation = data.observation;
+          obs.metadata = {
+            fire_sources:    data.observation.fire_sources_count ?? 0,
+            fire_spread_rate:data.observation.fire_spread_rate   ?? 0,
+            humidity:        data.observation.humidity           ?? 0,
+            difficulty:      'medium',
+          };
+          applyObservation(obs);
+          updateReport('reset', payload, data);
+          setStatusMsg(`System Ready — ${doorCount} door(s) detected.`);
+          pushLog(`Layout found (${doorCount} doors). Doors detected.`, obs.map_state.step_count, 0);
+          return;
+        }
+      }
+      setStatusMsg('Optimal layout not found after 8 attempts.', true);
+    } catch (err: any) {
+      setStatusMsg(`Search Failed: ${err.message}`, true);
+    }
+  };
+
+  const runAction = async (actionObj: unknown, label: string) => {
+    try {
+      setStatusMsg(`Action: ${label}`);
+      const payload = actionObj;
+      const data    = await apiCall('/step', payload);
+      const obs: Observation = data.observation;
+      obs.metadata = observation?.metadata;
+      applyObservation(obs);
+      updateReport('step', payload, data);
+      const rwd = Number(data.reward || 0);
+      setStatusMsg(`Executed. Reward: ${rwd.toFixed(2)}`);
+      pushLog(
+        obs.last_action_feedback || label,
+        obs.map_state.step_count,
+        rwd,
+        rwd < -0.5,
+      );
+      if (data.done) setIsAutoWait(false);
+    } catch (err: any) {
+      setStatusMsg(`Error: ${err.message}`, true);
+    }
+  };
+
+  const fetchAndApplyScene = useCallback(async () => {
+    try {
+      const res = await fetch('/scene');
+      if (!res.ok) throw new Error(`${res.status} ${res.statusText}`);
+      const scene: SceneResponse = await res.json();
+      setSceneData(scene);
+      updateReport('scene', {}, scene);
+
+      const { labels, graph } = scene;
+      const cell_grid:  number[] = [];
+      const fire_grid:  number[] = [];
+      const smoke_grid: number[] = [];
+
+      for (let y = 0; y < graph.height; y++) {
+        for (let x = 0; x < graph.width; x++) {
+          const [type, fire, smoke] = graph.grid[y][x];
+          cell_grid.push(type);
+          fire_grid.push(fire);
+          smoke_grid.push(smoke);
+        }
+      }
+
+      const visible_cells: [number, number][] = [];
+      for (let y = 0; y < graph.height; y++) {
+        for (let x = 0; x < graph.width; x++) {
+          if (graph.grid[y][x][4] === 1.0) visible_cells.push([x, y]);
+        }
+      }
+
+      const pseudoObs: Observation = {
+        map_state: {
+          cell_grid, fire_grid, smoke_grid,
+          agent_x:      labels.agent.x,
+          agent_y:      labels.agent.y,
+          visible_cells,
+          door_registry:  labels.map.door_registry,
+          exit_positions: labels.map.exit_positions,
+          step_count:     labels.episode.step,
+          max_steps:      labels.episode.max_steps,
+          grid_w:         graph.width,
+          grid_h:         graph.height,
+          template_name:  labels.episode.template,
+        },
+        agent_health:         labels.agent.health,
+        location_label:       labels.agent.location,
+        smoke_level:          labels.agent.smoke_level,
+        wind_dir:             labels.episode.wind_dir,
+        fire_visible:         labels.agent.fire_visible,
+        fire_direction:       labels.agent.fire_direction,
+        last_action_feedback: labels.agent.last_action_feedback,
+        narrative: '',
+        metadata: {
+          fire_sources:    labels.episode.fire_sources,
+          fire_spread_rate:labels.episode.fire_spread_rate,
+          humidity:        labels.episode.humidity,
+          difficulty:      labels.episode.difficulty,
+        },
+      };
+      applyObservation(pseudoObs);
+    } catch (err: any) {
+      setStatusMsg(`Sync Error: ${err.message}`, true);
+    }
+  }, [applyObservation, observation?.metadata]);
+
+  useEffect(() => {
+    if (isAutoWait) {
+      autoWaitTimer.current = window.setInterval(() => runAction({ action: 'wait' }, 'AUTO WAIT'), 900);
+    } else {
+      if (autoWaitTimer.current) clearInterval(autoWaitTimer.current);
+    }
+    return () => { if (autoWaitTimer.current) clearInterval(autoWaitTimer.current); };
+  }, [isAutoWait]);
+
+  useEffect(() => {
+    if (isPolling) {
+      fetchAndApplyScene();
+      pollTimer.current = window.setInterval(fetchAndApplyScene, 500);
+    } else {
+      if (pollTimer.current) clearInterval(pollTimer.current);
+    }
+    return () => { if (pollTimer.current) clearInterval(pollTimer.current); };
+  }, [isPolling, fetchAndApplyScene]);
+
+  useEffect(() => {
+    if (agentMoveFlash > 0) {
+      const timer = setTimeout(() => setAgentMoveFlash(f => f - 1), 50);
+      return () => clearTimeout(timer);
+    }
+  }, [agentMoveFlash]);
+
+  const setup = async () => {
+    setIsPolling(false);
+    setAgentMoveCount(0);
+    setAgentMoveFlash(0);
+    setEventLog([]);
+    prevAgentPos.current = { x: -1, y: -1 };
+    await resetLive();
+    setIsPolling(true);
+  };
+
+  /* Derived state */
+  const doors: Door[] = Object.entries(observation?.map_state.door_registry || {})
+    .map(([id, [x, y]]) => {
+      const ct = observation?.map_state.cell_grid[y * (observation?.map_state.grid_w ?? 16) + x];
+      let state: 'open' | 'closed' | 'failed' = 'open';
+      if (ct === DOOR_CLOSED) state = 'closed';
+      if (ct === OBSTACLE)   state = 'failed';
+      return { id, x, y, state };
+    })
+    .sort((a, b) => a.id.localeCompare(b.id, undefined, { numeric: true }));
+
+  const fireCells   = observation?.map_state.fire_grid.filter(v => v > 0.05).length ?? 0;
+  const exploredPct = observation
+    ? Math.round((new Set(observation.map_state.visible_cells.map(([vx, vy]) => `${vx},${vy}`)).size
+        / observation.map_state.cell_grid.length) * 100)
+    : 0;
+
+  const hp        = Math.round(observation?.agent_health ?? 0);
+  const hpColor   = hp >= 60 ? 'var(--green)' : hp >= 30 ? 'var(--amber)' : 'var(--red)';
+  const isOnline  = isPolling && !isError;
+  const epId      = sceneData?.labels.episode.id?.slice(0, 8) ?? '—';
+
+  return (
+    <div className="shell">
+      {/* ── Topbar ── */}
+      <header className="topbar">
+        <div className="brand">
+          <div className="brand-icon">🔥</div>
+          <span className="brand-name">Pyre</span>
+          <span className="brand-sep">/</span>
+          <span className="brand-sub">Crisis Navigation</span>
+        </div>
+
+        <div className="topbar-right">
+          <div className="topbar-ep">
+            Episode <span className="ep-id">{epId}</span>
+          </div>
+          <span className={`live-chip ${isError ? 'error' : isOnline ? 'online' : 'offline'}`}>
+            {isError ? 'Error' : isOnline ? 'Live' : 'Idle'}
+          </span>
+        </div>
+      </header>
+
+      {/* ── Body ── */}
+      <div className="content">
+
+        {/* ── Left: Canvas Zone ── */}
+        <div className="canvas-zone">
+          <div className="canvas-frame">
+            <Map2D observation={observation} agentMoveFlash={agentMoveFlash} />
+            <HUD observation={observation} agentMoveCount={agentMoveCount} />
+          </div>
+
+          {/* Legend */}
+          <div className="legend">
+            {[
+              { color:'#5e5850', label:'Wall'      },
+              { color:'#3a3530', label:'Obstacle'  },
+              { color:'#e6f4ec', label:'Exit'      },
+              { color:'#7c5c3c', label:'Door'      },
+              { color:'#f97316', label:'Fire'      },
+              { color:'rgba(72,82,96,0.7)', label:'Smoke' },
+              { color:'#3b82f6', label:'Agent'     },
+              { color:'rgba(2,132,199,0.6)', label:'Trail'},
+            ].map(({ color, label }) => (
+              <div key={label} className="legend-item">
+                <div className="leg-swatch" style={{ background: color, border:'1px solid rgba(0,0,0,0.1)' }} />
+                {label}
+              </div>
+            ))}
+          </div>
+
+          {/* Field Report */}
+          <div className="dialog">
+            <span className="dialog-who">Field Report</span>
+            {observation?.last_action_feedback || 'Establishing link to field systems...'}
+          </div>
+        </div>
+
+        {/* ── Right: Side Panel ── */}
+        <aside className="side">
+
+          {/* Controls */}
+          <ControlPanel
+            onAction={runAction}
+            onReset={resetLive}
+            onResetDoors={resetUntilDoors}
+            onSetup={setup}
+            doors={doors}
+            isAutoWait={isAutoWait}
+            toggleAutoWait={() => setIsAutoWait(!isAutoWait)}
+            isPolling={isPolling}
+            togglePolling={() => setIsPolling(!isPolling)}
+            status={status}
+            isError={isError}
+          />
+
+          {/* Agent Biometrics */}
+          <div className="side-sec">
+            <div className="sec-hd">Agent Biometrics</div>
+            <div className="sg">
+              <div className="sc">
+                <div className="sc-l">Health</div>
+                <div className="sc-v" style={{ color: hpColor }}>{hp}%</div>
+              </div>
+              <div className="sc">
+                <div className="sc-l">Status</div>
+                <div className="sc-v" style={{ color: hpColor }}>
+                  {sceneData?.labels.agent.health_status ?? 'NOMINAL'}
+                </div>
+              </div>
+              <div className="sc">
+                <div className="sc-l">Position</div>
+                <div className="sc-v blue">
+                  ({observation?.map_state.agent_x ?? '—'},{observation?.map_state.agent_y ?? '—'})
+                </div>
+              </div>
+              <div className="sc">
+                <div className="sc-l">Sector</div>
+                <div className="sc-v">{observation?.location_label ?? 'Unknown'}</div>
+              </div>
+            </div>
+            <div className="bar-w">
+              <div className="bar-lbl">
+                <span>System Integrity</span>
+                <span>{hp}%</span>
+              </div>
+              <div className="bar-bg">
+                <div className="bar-fill" style={{ width: `${hp}%`, background: hpColor }} />
+              </div>
+            </div>
+          </div>
+
+          {/* Environment */}
+          <div className="side-sec">
+            <div className="sec-hd">Environment</div>
+            <div className="sg">
+              <div className="sc">
+                <div className="sc-l">Hazard Cells</div>
+                <div className="sc-v fire">{fireCells}</div>
+              </div>
+              <div className="sc">
+                <div className="sc-l">Explored</div>
+                <div className="sc-v blue">{exploredPct}%</div>
+              </div>
+              <div className="sc">
+                <div className="sc-l">Wind</div>
+                <div className="sc-v">{observation?.wind_dir ?? 'CALM'}</div>
+              </div>
+              <div className="sc">
+                <div className="sc-l">Humidity</div>
+                <div className="sc-v amber">
+                  {Math.round((observation?.metadata?.humidity ?? 0) * 100)}%
+                </div>
+              </div>
+            </div>
+          </div>
+
+          {/* Event Log */}
+          <div className="side-sec" style={{ flex: 1, minHeight: 0, display: 'flex', flexDirection: 'column' }}>
+            <div className="sec-hd">
+              Event Log
+              <span style={{ fontFamily: 'var(--mono)', fontSize: '9px', color: 'var(--t3)' }}>
+                {eventLog.length} events
+              </span>
+            </div>
+            <div className="elog">
+              {eventLog.length === 0 && (
+                <div style={{ color: 'var(--t3)', fontFamily: 'var(--mono)', fontSize: '10px', padding: '4px' }}>
+                  No events yet…
+                </div>
+              )}
+              {eventLog.map((e, i) => (
+                <div key={i} className={`erow ${e.isAlarm ? 'alarm' : ''}`}>
+                  <span className="estep">T{e.step}</span>
+                  <span className="etext">{e.text}</span>
+                  <span className={`erwd ${e.reward >= 0 ? 'p' : 'n'}`}>
+                    {e.reward >= 0 ? '+' : ''}{e.reward.toFixed(2)}
+                  </span>
+                </div>
+              ))}
+              <div ref={logEndRef} />
+            </div>
+          </div>
+
+          {/* API Report */}
+          <div className="side-sec">
+            <div className="sec-hd">Network Activity</div>
+            <APIReport report={apiReport} onCopyReset={() => {}} onCopyStep={() => {}} onCopyScene={() => {}} />
+          </div>
+
+        </aside>
+      </div>
+    </div>
+  );
+}
+
+export default App;

frontend/src/components/APIReport.tsx

@@ -0,0 +1,33 @@
+import React from 'react';
+import type { ApiReport } from '../types';
+
+interface APIReportProps {
+  report: ApiReport | null;
+  onCopyReset: () => void;
+  onCopyStep: () => void;
+  onCopyScene: () => void;
+}
+
+const APIReport: React.FC<APIReportProps> = ({ report, onCopyReset, onCopyStep, onCopyScene }) => {
+  return (
+    <div>
+      <div className="report-meta" style={{ marginBottom: '8px' }}>
+        {report ? report.meta : 'Awaiting telemetry...'}
+      </div>
+      <div className="report-box">
+        {report ? JSON.stringify({
+          call_type: report.call_type,
+          request: report.request,
+          response: report.response
+        }, null, 2) : '{}'}
+      </div>
+      <div className="ctrl-grid" style={{ marginTop: '12px' }}>
+        <button className="ctrl-btn" onClick={onCopyScene}>Scene</button>
+        <button className="ctrl-btn" onClick={onCopyReset}>Reset</button>
+        <button className="ctrl-btn" onClick={onCopyStep}>Step</button>
+      </div>
+    </div>
+  );
+};
+
+export default APIReport;

frontend/src/components/ControlPanel.tsx

@@ -0,0 +1,90 @@
+import React from 'react';
+import type { Door } from '../types';
+
+interface ControlPanelProps {
+  onAction: (action: unknown, label: string) => void;
+  onReset: (difficulty: string) => void;
+  onResetDoors: () => void;
+  onSetup: () => void;
+  doors: Door[];
+  isAutoWait: boolean;
+  toggleAutoWait: () => void;
+  isPolling: boolean;
+  togglePolling: () => void;
+  status: string;
+  isError: boolean;
+}
+
+const ControlPanel: React.FC<ControlPanelProps> = ({
+  onAction, onSetup, doors,
+  isAutoWait, toggleAutoWait,
+  isPolling, togglePolling,
+  status, isError,
+}) => {
+  const mv = (dir: string) => onAction({ action: 'move', direction: dir.toLowerCase() }, `MOVE ${dir.toUpperCase()}`);
+
+  return (
+    <div className="side-sec">
+      <div className="sec-hd">Tactical Controls</div>
+
+      {/* D-Pad */}
+      <div className="ctrl-grid" style={{ maxWidth: 160, margin: '0 auto', marginBottom: 8 }}>
+        {/* Row 1 */}
+        <div />
+        <button className="ctrl-btn" onClick={() => mv('north')}>▲</button>
+        <div />
+        {/* Row 2 */}
+        <button className="ctrl-btn" onClick={() => mv('west')}>◀</button>
+        <button className="ctrl-btn accent" onClick={() => onAction({ action: 'wait' }, 'WAIT')} title="Wait in place">●</button>
+        <button className="ctrl-btn" onClick={() => mv('east')}>▶</button>
+        {/* Row 3 */}
+        <div />
+        <button className="ctrl-btn" onClick={() => mv('south')}>▼</button>
+        <div />
+      </div>
+
+      {/* Secondary actions */}
+      <div className="ctrl-row">
+        <button className="ctrl-btn" onClick={() => onAction({ action: 'look' }, 'LOOK')}>SCAN</button>
+        <button className={`ctrl-btn ${isAutoWait ? 'active' : ''}`} onClick={toggleAutoWait}>
+          {isAutoWait ? 'STOP AUTO' : 'AUTO'}
+        </button>
+        <button className={`ctrl-btn ${isPolling ? 'active' : ''}`} onClick={togglePolling}>
+          {isPolling ? 'LIVE ●' : 'LIVE ○'}
+        </button>
+      </div>
+
+      {/* Reboot */}
+      <div className="ctrl-row" style={{ marginTop: 6 }}>
+        <button className="ctrl-btn play" onClick={onSetup} style={{ flex: 'none', width: '100%' }}>
+          ↺ REBOOT EPISODE
+        </button>
+      </div>
+
+      {/* Status line */}
+      <div className={`ctrl-status ${isError ? 'error' : ''}`}>
+        {status}
+      </div>
+
+      {/* Doors */}
+      {doors.length > 0 && (
+        <div style={{ marginTop: 12 }}>
+          <div className="sec-hd" style={{ marginBottom: 6 }}>Proximity Doors</div>
+          <div className="door-grid">
+            {doors.map(d => (
+              <button
+                key={d.id}
+                className={`door-btn ${d.state}`}
+                onClick={() => onAction({ action: 'door', target_id: d.id, door_state: d.state === 'closed' ? 'open' : 'close' }, `DOOR ${d.id}`)}
+              >
+                {d.id} [{d.state}]
+              </button>
+            ))}
+          </div>
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default ControlPanel;

frontend/src/components/HUD.tsx

@@ -0,0 +1,169 @@
+import React, { useEffect, useRef, useState } from 'react';
+import type { Observation } from '../types';
+
+interface HUDProps {
+  observation: Observation | null;
+  agentMoveCount?: number;
+}
+
+/* Unicode compass arrows for each wind direction */
+const WIND_ARROW: Record<string, string> = {
+  N: '↑', S: '↓', E: '→', W: '←',
+  NE: '↗', NW: '↖', SE: '↘', SW: '↙',
+  CALM: '·',
+};
+
+/* Rotation degrees so the arrow visually points in the right direction */
+const WIND_DEG: Record<string, number> = {
+  N: 0, NE: 45, E: 90, SE: 135,
+  S: 180, SW: 225, W: 270, NW: 315, CALM: 0,
+};
+
+const HUD: React.FC<HUDProps> = ({ observation, agentMoveCount = 0 }) => {
+  const [firePulse, setFirePulse] = useState(0);
+  const pulseRef = useRef(0);
+
+  useEffect(() => {
+    let raf: number;
+    const tick = () => {
+      pulseRef.current += 0.05;
+      setFirePulse(Math.sin(pulseRef.current * 4) * 0.5 + 0.5);
+      raf = requestAnimationFrame(tick);
+    };
+    raf = requestAnimationFrame(tick);
+    return () => cancelAnimationFrame(raf);
+  }, []);
+
+  if (!observation) return null;
+
+  const { map_state, agent_health, smoke_level, wind_dir, fire_visible, metadata } = observation;
+  const hPct = Math.round(agent_health);
+  const sPct = Math.round((map_state.step_count / map_state.max_steps) * 100);
+  const totalFireCells = map_state.fire_grid.filter(v => v > 0.05).length;
+
+  let hBarClass    = 'g';
+  let hStatusLabel = 'Nominal';
+  let hStatusClass = 'good';
+
+  if (hPct < 30)      { hBarClass = 'c'; hStatusLabel = 'Critical'; hStatusClass = 'critical'; }
+  else if (hPct < 60) { hBarClass = 'm'; hStatusLabel = 'Moderate'; hStatusClass = 'moderate'; }
+
+  const windDir    = wind_dir || 'CALM';
+  const windArrow  = WIND_ARROW[windDir] ?? '?';
+  const windDeg    = WIND_DEG[windDir]   ?? 0;
+  const spreadRate = metadata?.fire_spread_rate ?? 0;
+  const humidity   = metadata?.humidity ?? 0;
+
+  return (
+    <div className="hud-overlay">
+      {/* ── Left: Health ── */}
+      <div className="hud-card">
+        <div className="hud-r">
+          <span className="hlbl">HP</span>
+          <div className="hbar-bg">
+            <div className={`hbar-fill ${hBarClass}`} style={{ width: `${hPct}%` }} />
+          </div>
+          <span className="hval">{hPct}</span>
+        </div>
+        <div className="hud-r" style={{ gap: '8px', marginTop: '2px' }}>
+          <span className={`hstatus ${hStatusClass}`}>{hStatusLabel}</span>
+          <span style={{ fontFamily: 'var(--mono)', fontSize: '9px', color: 'rgba(168,162,158,.55)', marginLeft: 'auto' }}>
+            💨 {smoke_level || 'clear'}
+          </span>
+        </div>
+        {agentMoveCount > 0 && (
+          <div style={{ fontFamily: 'var(--mono)', fontSize: '8px', color: 'rgba(168,162,158,.4)', marginTop: '2px' }}>
+            moves: {agentMoveCount}
+          </div>
+        )}
+      </div>
+
+      {/* ── Center: Wind & Hazard ── */}
+      <div className="hud-card hud-card-center">
+        {/* Compass rose */}
+        <div style={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
+          <div style={{ position: 'relative', width: 32, height: 32, flexShrink: 0 }}>
+            {/* compass ring */}
+            <svg width="32" height="32" viewBox="0 0 32 32" style={{ position: 'absolute', top: 0, left: 0 }}>
+              <circle cx="16" cy="16" r="14" fill="none" stroke="rgba(255,255,255,0.12)" strokeWidth="1.5" />
+              <text x="16" y="6"  textAnchor="middle" fontSize="5" fill="rgba(255,255,255,0.35)" dominantBaseline="middle">N</text>
+              <text x="16" y="28" textAnchor="middle" fontSize="5" fill="rgba(255,255,255,0.35)" dominantBaseline="middle">S</text>
+              <text x="4"  y="17" textAnchor="middle" fontSize="5" fill="rgba(255,255,255,0.35)" dominantBaseline="middle">W</text>
+              <text x="28" y="17" textAnchor="middle" fontSize="5" fill="rgba(255,255,255,0.35)" dominantBaseline="middle">E</text>
+            </svg>
+            {/* direction arrow */}
+            <div style={{
+              position: 'absolute', top: '50%', left: '50%',
+              transform: `translate(-50%,-50%) rotate(${windDeg}deg)`,
+              fontSize: windDir === 'CALM' ? '10px' : '14px',
+              color: windDir === 'CALM' ? 'rgba(168,162,158,0.6)' : '#fbbf24',
+              lineHeight: 1,
+              transition: 'transform 0.6s ease',
+            }}>
+              {windArrow}
+            </div>
+          </div>
+
+          <div>
+            <div className="hlbl">Wind</div>
+            <div style={{ fontFamily: 'var(--mono)', fontSize: '13px', fontWeight: 500, color: '#f0e8e0', lineHeight: 1.2 }}>
+              {windDir}
+            </div>
+          </div>
+        </div>
+
+        <div style={{ display: 'flex', gap: '10px', marginTop: '4px' }}>
+          <div>
+            <div className="hlbl">Spread</div>
+            <div style={{ fontFamily: 'var(--mono)', fontSize: '11px', color: spreadRate > 0.5 ? '#f87171' : '#fbbf24' }}>
+              {(spreadRate * 100).toFixed(0)}%
+            </div>
+          </div>
+          <div>
+            <div className="hlbl">Humidity</div>
+            <div style={{ fontFamily: 'var(--mono)', fontSize: '11px', color: humidity > 0.6 ? '#60a5fa' : '#a8a29e' }}>
+              {(humidity * 100).toFixed(0)}%
+            </div>
+          </div>
+          {totalFireCells > 0 && (
+            <div style={{ alignSelf: 'center' }}>
+              <span style={{
+                fontFamily: 'var(--mono)', fontSize: '9px', fontWeight: 700,
+                color: fire_visible ? '#fff' : '#fbbf24',
+                background: fire_visible
+                  ? `rgba(239,${Math.floor(30 + firePulse * 40)},0,${0.75 + firePulse * 0.25})`
+                  : `rgba(180,60,0,${0.55 + firePulse * 0.3})`,
+                border: fire_visible
+                  ? `1px solid rgba(255,${Math.floor(60 + firePulse * 80)},0,0.8)`
+                  : '1px solid rgba(251,191,36,0.5)',
+                padding: '2px 6px', borderRadius: '3px', letterSpacing: '0.06em',
+                boxShadow: fire_visible
+                  ? `0 0 ${6 + firePulse * 8}px rgba(255,80,0,0.7)`
+                  : `0 0 ${3 + firePulse * 4}px rgba(200,80,0,0.4)`,
+                display: 'flex', alignItems: 'center', gap: '3px',
+                transition: 'box-shadow 0.1s',
+              }}>
+                🔥 {fire_visible ? 'IN RANGE' : 'ACTIVE'} · {totalFireCells}
+              </span>
+            </div>
+          )}
+        </div>
+      </div>
+
+      {/* ── Right: Steps ── */}
+      <div className="hud-card" style={{ textAlign: 'right', minWidth: 'auto' }}>
+        <div className="hud-r" style={{ justifyContent: 'flex-end' }}>
+          <span className="step-val">{map_state.step_count} / {map_state.max_steps}</span>
+        </div>
+        <div className="sbar-bg">
+          <div className="sbar-fill" style={{ width: `${sPct}%` }} />
+        </div>
+        <div className="step-meta">
+          {metadata?.difficulty ?? 'medium'} · {map_state.template_name}
+        </div>
+      </div>
+    </div>
+  );
+};
+
+export default HUD;

frontend/src/components/Map2D.tsx

@@ -0,0 +1,600 @@
+import React, { useRef, useEffect } from 'react';
+import type { Observation } from '../types';
+
+interface Map2DProps {
+  observation: Observation | null;
+  agentMoveFlash: number;
+}
+
+/* ── Cell type constants ── */
+const WALL        = 1;
+const DOOR_OPEN   = 2;
+const DOOR_CLOSED = 3;
+const EXIT        = 4;
+const OBSTACLE    = 5;
+
+/* ── Wind direction vectors ── */
+const WIND_DIRS: Record<string, [number, number]> = {
+  N: [0, -1], S: [0, 1], E: [1, 0], W: [-1, 0],
+  NW: [-0.7, -0.7], NE: [0.7, -0.7], SW: [-0.7, 0.7], SE: [0.7, 0.7],
+  CALM: [0, 0],
+};
+
+/* ── Agent appearance per health tier ── */
+const AGENT_THEMES = {
+  healthy:  { body: '#3b82f6', dark: '#1d4ed8', arm: '#2563eb', ring: '#fbbf24', ringGlow: 'rgba(251,191,36,0.5)' },
+  moderate: { body: '#f97316', dark: '#c2410c', arm: '#ea580c', ring: '#fb923c', ringGlow: 'rgba(251,146,60,0.5)' },
+  low:      { body: '#dc2626', dark: '#991b1b', arm: '#b91c1c', ring: '#f87171', ringGlow: 'rgba(248,113,113,0.5)' },
+  critical: { body: '#7c3aed', dark: '#5b21b6', arm: '#6d28d9', ring: '#c4b5fd', ringGlow: 'rgba(196,181,253,0.5)' },
+};
+
+/* ── Ember particle ── */
+class Ember {
+  x: number; y: number; vx: number; vy: number;
+  life: number; decay: number; size: number;
+  type: 'ember' | 'spark';
+
+  constructor(x: number, y: number, windX: number) {
+    const speed = 0.4 + Math.random() * 1.0;
+    const angle = -Math.PI / 2 + (Math.random() - 0.5) * 1.6;
+    this.x = x + (Math.random() - 0.5) * 3;
+    this.y = y + (Math.random() - 0.5) * 3;
+    this.vx = Math.cos(angle) * speed + windX * 0.7;
+    this.vy = Math.sin(angle) * speed - 0.22;
+    this.life = 1.0;
+    this.decay = 0.012 + Math.random() * 0.015;
+    this.size = 1.2 + Math.random() * 2.2;
+    this.type = Math.random() > 0.4 ? 'ember' : 'spark';
+  }
+
+  update() {
+    this.x += this.vx;
+    this.y += this.vy;
+    this.vy -= 0.012;
+    this.vx *= 0.97;
+    this.life -= this.decay;
+  }
+}
+
+
+/* ── Minecraft pixel-art character ── */
+function drawMinecraftAgent(
+  ctx: CanvasRenderingContext2D,
+  cx: number, cy: number, cs: number,
+  theme: typeof AGENT_THEMES.healthy
+) {
+  const u = cs / 18;
+  const left = cx - 5 * u;
+  const top  = cy - 8.5 * u;
+
+  const px = (rx: number, ry: number, rw: number, rh: number, color: string) => {
+    ctx.fillStyle = color;
+    ctx.fillRect(left + rx * u, top + ry * u, rw * u, rh * u);
+  };
+
+  /* Helmet */
+  px(2, 0, 6, 1, '#5c4a3d');
+  /* Head */
+  px(2, 1, 6, 5, '#f5d5a0');
+  /* Face features */
+  px(3, 3, 1, 1, '#3d2b1a'); /* left eye */
+  px(6, 3, 1, 1, '#3d2b1a'); /* right eye */
+  px(4, 5, 2, 1, '#c8937a'); /* mouth */
+  /* Hair accent */
+  px(2, 1, 6, 1, '#7a5c3e');
+
+  /* Body */
+  px(3, 6, 4, 4, theme.body);
+  px(3, 6, 4, 1, theme.dark);
+
+  /* Arms */
+  px(1, 6, 2, 4, theme.arm);
+  px(7, 6, 2, 4, theme.arm);
+
+  /* Legs */
+  px(3, 10, 2, 4, '#1e40af');
+  px(5, 10, 2, 4, '#1e3a8a');
+
+  /* Boots */
+  px(3, 14, 2, 2, '#3a2e26');
+  px(5, 14, 2, 2, '#2e2420');
+}
+
+/* ── Main canvas component ── */
+const Map2D: React.FC<Map2DProps> = ({ observation, agentMoveFlash }) => {
+  const canvasRef = useRef<HTMLCanvasElement>(null);
+  const embersRef = useRef<Ember[]>([]);
+  const trailRef  = useRef<{ x: number; y: number; t: number }[]>([]);
+  const timeRef   = useRef(0);
+  const rafRef    = useRef(0);
+
+  const CS = 40;
+
+  const animate = () => {
+    const canvas = canvasRef.current;
+    if (!canvas || !observation) { rafRef.current = requestAnimationFrame(animate); return; }
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+
+    timeRef.current += 0.016;
+    const t = timeRef.current;
+
+    const { map_state, agent_health, wind_dir } = observation;
+    const { grid_w: W, grid_h: H, cell_grid, fire_grid, smoke_grid, agent_x, agent_y } = map_state;
+    const cs = CS;
+    const wv = WIND_DIRS[wind_dir] ?? [0, 0];
+    const idx = (x: number, y: number) => y * W + x;
+    const visible = new Set(map_state.visible_cells.map(([vx, vy]) => `${vx},${vy}`));
+
+    /* ── Canvas bg ── */
+    ctx.fillStyle = '#c8b890';
+    ctx.fillRect(0, 0, canvas.width, canvas.height);
+
+    /* ── Base layer ── */
+    for (let y = 0; y < H; y++) {
+      for (let x = 0; x < W; x++) {
+        const ct = cell_grid[idx(x, y)];
+        const px = x * cs, py = y * cs;
+
+        switch (ct) {
+          case WALL: {
+            /* Animated heat tint — walls near fire glow ember-red */
+            const nearFire = fire_grid[idx(x, y)] > 0.05
+              ? fire_grid[idx(x, y)]
+              : (
+                  (x > 0     && fire_grid[idx(x-1, y)] > 0.05 ? fire_grid[idx(x-1, y)] : 0) +
+                  (x < W-1   && fire_grid[idx(x+1, y)] > 0.05 ? fire_grid[idx(x+1, y)] : 0) +
+                  (y > 0     && fire_grid[idx(x, y-1)] > 0.05 ? fire_grid[idx(x, y-1)] : 0) +
+                  (y < H-1   && fire_grid[idx(x, y+1)] > 0.05 ? fire_grid[idx(x, y+1)] : 0)
+                ) * 0.28;
+
+            const heatShift = Math.min(1, nearFire * 2.2);
+            const wallFlicker = 0.88 + 0.12 * Math.sin(t * 7.3 + x * 2.1 + y * 3.7);
+
+            /* Base stone colour, heat-shifted toward deep orange-red */
+            const br = Math.round(94  + heatShift * 100 * wallFlicker);
+            const bg = Math.round(88  - heatShift * 52);
+            const bb = Math.round(80  - heatShift * 70);
+            ctx.fillStyle = `rgb(${br},${bg},${bb})`;
+            ctx.fillRect(px, py, cs, cs);
+
+            /* Brick rows — two horizontal bands */
+            const brickH = cs / 2;
+            for (let row = 0; row < 2; row++) {
+              const by = py + row * brickH;
+              /* mortar gap between rows */
+              ctx.fillStyle = `rgba(0,0,0,${0.28 + heatShift * 0.12})`;
+              ctx.fillRect(px, by + brickH - 1, cs, 1);
+              /* vertical mortar — staggered per row */
+              const mortarX = px + ((x + row) % 2 === 0 ? cs / 2 : cs / 4);
+              ctx.fillStyle = `rgba(0,0,0,${0.22 + heatShift * 0.10})`;
+              ctx.fillRect(mortarX, by, 1, brickH - 1);
+            }
+
+            /* Top-left highlight bevel */
+            ctx.fillStyle = `rgba(255,${200 - Math.round(heatShift * 80)},${160 - Math.round(heatShift * 140)},${0.32 + heatShift * 0.15})`;
+            ctx.fillRect(px, py, cs, 2);
+            ctx.fillRect(px, py + 2, 2, cs - 2);
+
+            /* Bottom-right shadow bevel */
+            ctx.fillStyle = `rgba(0,0,0,${0.50 + heatShift * 0.20})`;
+            ctx.fillRect(px, py + cs - 2, cs, 2);
+            ctx.fillRect(px + cs - 2, py, 2, cs - 2);
+
+            /* Heat glow overlay on wall face */
+            if (heatShift > 0.05) {
+              const glowA = heatShift * 0.35 * wallFlicker;
+              ctx.fillStyle = `rgba(255,${Math.round(80 - heatShift * 60)},0,${glowA})`;
+              ctx.fillRect(px + 2, py + 2, cs - 4, cs - 4);
+
+              /* Hot crack lines radiating from fire side */
+              ctx.strokeStyle = `rgba(255,${Math.round(160 - heatShift * 120)},0,${heatShift * 0.6 * wallFlicker})`;
+              ctx.lineWidth = 1;
+              ctx.beginPath();
+              ctx.moveTo(px + cs * 0.3, py + cs * 0.2);
+              ctx.lineTo(px + cs * 0.5, py + cs * 0.55);
+              ctx.lineTo(px + cs * 0.7, py + cs * 0.4);
+              ctx.stroke();
+              if (heatShift > 0.4) {
+                ctx.beginPath();
+                ctx.moveTo(px + cs * 0.2, py + cs * 0.7);
+                ctx.lineTo(px + cs * 0.45, py + cs * 0.85);
+                ctx.stroke();
+              }
+            }
+            break;
+          }
+          case OBSTACLE: {
+            /* Charred debris — dark with ember glow */
+            const obsNearFire = (
+              (x > 0   && fire_grid[idx(x-1, y)] > 0.05 ? fire_grid[idx(x-1, y)] : 0) +
+              (x < W-1 && fire_grid[idx(x+1, y)] > 0.05 ? fire_grid[idx(x+1, y)] : 0) +
+              (y > 0   && fire_grid[idx(x, y-1)] > 0.05 ? fire_grid[idx(x, y-1)] : 0) +
+              (y < H-1 && fire_grid[idx(x, y+1)] > 0.05 ? fire_grid[idx(x, y+1)] : 0)
+            ) * 0.4 + fire_grid[idx(x, y)] * 0.8;
+            const obsHeat = Math.min(1, obsNearFire);
+            const obsFlicker = 0.82 + 0.18 * Math.sin(t * 9.1 + x * 1.9 + y * 2.5);
+
+            ctx.fillStyle = '#2a2520';
+            ctx.fillRect(px, py, cs, cs);
+
+            /* Rubble texture patches */
+            ctx.fillStyle = 'rgba(60,50,40,0.7)';
+            ctx.fillRect(px + 3, py + 3, cs * 0.4, cs * 0.35);
+            ctx.fillRect(px + cs * 0.55, py + cs * 0.5, cs * 0.35, cs * 0.4);
+            ctx.fillStyle = 'rgba(80,65,50,0.5)';
+            ctx.fillRect(px + cs * 0.25, py + cs * 0.6, cs * 0.45, cs * 0.3);
+
+            /* Ember glow if near fire */
+            if (obsHeat > 0.05) {
+              const eg = Math.round(40 + obsHeat * 90 * obsFlicker);
+              ctx.fillStyle = `rgba(255,${eg},0,${obsHeat * 0.55 * obsFlicker})`;
+              ctx.fillRect(px + 2, py + 2, cs - 4, cs - 4);
+
+              /* Glowing edge cracks */
+              ctx.strokeStyle = `rgba(255,${Math.round(120 * obsHeat * obsFlicker)},0,${obsHeat * 0.8})`;
+              ctx.lineWidth = 1.5;
+              ctx.beginPath();
+              ctx.moveTo(px + cs * 0.1, py + cs * 0.5);
+              ctx.lineTo(px + cs * 0.4, py + cs * 0.3);
+              ctx.lineTo(px + cs * 0.6, py + cs * 0.7);
+              ctx.lineTo(px + cs * 0.9, py + cs * 0.4);
+              ctx.stroke();
+            }
+
+            /* Orange danger frame */
+            ctx.strokeStyle = `rgba(255,${Math.round(80 + obsHeat * 60)},0,${0.55 + obsHeat * 0.35})`;
+            ctx.lineWidth = 2;
+            ctx.strokeRect(px + 1, py + 1, cs - 2, cs - 2);
+
+            /* Corner bolts */
+            ctx.fillStyle = `rgba(255,${Math.round(100 + obsHeat * 80)},0,${0.7 + obsHeat * 0.3})`;
+            [[4,4],[cs-6,4],[4,cs-6],[cs-6,cs-6]].forEach(([bx, by]) => {
+              ctx.beginPath(); ctx.arc(px+bx, py+by, 2, 0, Math.PI*2); ctx.fill();
+            });
+            break;
+          }
+          default: {
+            /* Checkerboard floor with warm heat tint near fire */
+            const floorFire = Math.min(1,
+              fire_grid[idx(x, y)] * 1.5 +
+              (x > 0   ? fire_grid[idx(x-1, y)] : 0) * 0.3 +
+              (x < W-1 ? fire_grid[idx(x+1, y)] : 0) * 0.3 +
+              (y > 0   ? fire_grid[idx(x, y-1)] : 0) * 0.3 +
+              (y < H-1 ? fire_grid[idx(x, y+1)] : 0) * 0.3
+            );
+            const base = (x + y) % 2 === 0;
+            const fr = Math.round((base ? 232 : 208) + floorFire * 23);
+            const fg = Math.round((base ? 216 : 190) - floorFire * 40);
+            const fb = Math.round((base ? 184 : 152) - floorFire * 80);
+            ctx.fillStyle = `rgb(${fr},${fg},${fb})`;
+            ctx.fillRect(px, py, cs, cs);
+            /* tile bevel */
+            ctx.fillStyle = 'rgba(255,255,255,0.20)';
+            ctx.fillRect(px, py, cs, 2);
+            ctx.fillRect(px, py + 2, 2, cs - 2);
+            ctx.fillStyle = 'rgba(0,0,0,0.18)';
+            ctx.fillRect(px, py + cs - 2, cs, 2);
+            ctx.fillRect(px + cs - 2, py, 2, cs - 2);
+          }
+        }
+      }
+    }
+
+    /* ── Fire ambient: multiply scorches the floor tiles (under fog) ── */
+    ctx.save();
+    ctx.globalCompositeOperation = 'multiply';
+    for (let y = 0; y < H; y++) {
+      for (let x = 0; x < W; x++) {
+        const fire = fire_grid[idx(x, y)];
+        if (fire < 0.1) continue;
+        const px = x * cs + cs / 2, py = y * cs + cs / 2;
+        const radius = cs * (1.2 + fire * 1.8);
+        const a = Math.min(0.85, fire * 0.9);
+        const gr = ctx.createRadialGradient(px, py, 0, px, py, radius);
+        gr.addColorStop(0,   `rgba(255,80,0,${a})`);
+        gr.addColorStop(0.4, `rgba(220,40,0,${a * 0.5})`);
+        gr.addColorStop(1,   'rgba(0,0,0,0)');
+        ctx.fillStyle = gr;
+        ctx.fillRect(px - radius, py - radius, radius * 2, radius * 2);
+      }
+    }
+    ctx.restore();
+
+    /* ── Smoke (dark on light bg, under fog) ── */
+    for (let y = 0; y < H; y++) {
+      for (let x = 0; x < W; x++) {
+        const smoke = smoke_grid[idx(x, y)];
+        if (smoke < 0.1) continue;
+        const px = x * cs + cs / 2, py = y * cs + cs / 2;
+        const offX = Math.sin(t * 0.5 + x) * 2;
+        const offY = Math.cos(t * 0.4 + y) * 2;
+        const alpha = Math.min(0.68, smoke * 0.8);
+        const gr = ctx.createRadialGradient(px + offX, py + offY, 0, px + offX, py + offY, cs * 0.82);
+        gr.addColorStop(0, `rgba(72,82,96,${alpha})`);
+        gr.addColorStop(1, 'rgba(72,82,96,0)');
+        ctx.fillStyle = gr;
+        ctx.beginPath(); ctx.arc(px + offX, py + offY, cs * 0.82, 0, Math.PI * 2); ctx.fill();
+      }
+    }
+
+    /* ── Exits & Doors ── */
+    for (let y = 0; y < H; y++) {
+      for (let x = 0; x < W; x++) {
+        const ct = cell_grid[idx(x, y)];
+        const px = x * cs, py = y * cs;
+        const pulse = 0.7 + 0.3 * Math.sin(t * 3);
+
+        if (ct === EXIT) {
+          ctx.fillStyle = '#e6f4ec';
+          ctx.fillRect(px + 2, py + 2, cs - 4, cs - 4);
+          ctx.strokeStyle = `rgba(22,163,74,${0.7 + 0.3 * pulse})`;
+          ctx.lineWidth = 2 * pulse;
+          ctx.strokeRect(px + 5, py + 5, cs - 10, cs - 10);
+          /* EXIT symbol */
+          ctx.fillStyle = `rgba(22,163,74,${0.85 + 0.15 * pulse})`;
+          ctx.font = `bold ${cs * 0.26}px var(--mono, monospace)`;
+          ctx.textAlign = 'center';
+          ctx.textBaseline = 'middle';
+          ctx.fillText('EXIT', px + cs / 2, py + cs / 2);
+        } else if (ct === DOOR_CLOSED) {
+          ctx.fillStyle = '#7c5c3c';
+          ctx.fillRect(px + 4, py + 2, cs - 8, cs - 4);
+          ctx.fillStyle = '#4a3020';
+          ctx.fillRect(px + 2, py, cs - 4, 2);
+          ctx.fillRect(px + 2, py + cs - 2, cs - 4, 2);
+          /* handle */
+          ctx.fillStyle = '#f0b030';
+          ctx.beginPath();
+          ctx.arc(px + cs - 10, py + cs / 2, 2.5, 0, Math.PI * 2);
+          ctx.fill();
+        } else if (ct === DOOR_OPEN) {
+          ctx.fillStyle = '#4a3020';
+          ctx.fillRect(px + 2, py, 4, cs);
+          ctx.fillRect(px + cs - 6, py, 4, cs);
+        }
+      }
+    }
+
+    /* ── Fog of War (dim only — fire still punches through above) ── */
+    for (let y = 0; y < H; y++) {
+      for (let x = 0; x < W; x++) {
+        const key = `${x},${y}`;
+        if (!visible.has(key)) {
+          ctx.fillStyle = 'rgba(140,134,126,0.55)';
+          ctx.fillRect(x * cs, y * cs, cs, cs);
+        }
+      }
+    }
+
+    /* ── Fire volumetric: drawn ABOVE fog — always visible ── */
+    for (let y = 0; y < H; y++) {
+      for (let x = 0; x < W; x++) {
+        const fire = fire_grid[idx(x, y)];
+        if (fire < 0.05) continue;
+        const px = x * cs + cs / 2, py = y * cs + cs / 2;
+        const flicker = 0.80 + 0.20 * Math.sin(t * 11.0 + x * 3.1 + y * 2.7);
+        const eff = Math.min(1, fire * flicker);
+        const isVisible = visible.has(`${x},${y}`);
+
+        const windDx = wv[0] * cs * 0.25 * eff;
+        const windDy = wv[1] * cs * 0.25 * eff - cs * 0.06;
+
+        /* Wide warning beacon glow for fire in fog — always shown */
+        if (!isVisible) {
+          const beaconPulse = 0.6 + 0.4 * Math.sin(t * 6.0 + x * 1.7 + y * 2.3);
+          const beaconR = cs * (1.4 + beaconPulse * 0.6);
+          const beaconGr = ctx.createRadialGradient(px, py, 0, px, py, beaconR);
+          beaconGr.addColorStop(0,   `rgba(255,100,0,${eff * beaconPulse * 0.85})`);
+          beaconGr.addColorStop(0.3, `rgba(255,60,0,${eff * beaconPulse * 0.50})`);
+          beaconGr.addColorStop(1,   'rgba(220,30,0,0)');
+          ctx.fillStyle = beaconGr;
+          ctx.beginPath(); ctx.arc(px, py, beaconR, 0, Math.PI * 2); ctx.fill();
+        }
+
+        /* Outer dark-red base */
+        {
+          const r = cs * 0.70 * (0.7 + eff * 0.3);
+          const cx2 = px + windDx * 0.5, cy2 = py + windDy * 0.5;
+          const gr = ctx.createRadialGradient(cx2, cy2, 0, cx2, cy2, r);
+          gr.addColorStop(0,   `rgba(200,20,0,${eff * 0.65})`);
+          gr.addColorStop(0.55,`rgba(170,10,0,${eff * 0.35})`);
+          gr.addColorStop(1,   'rgba(80,0,0,0)');
+          ctx.fillStyle = gr;
+          ctx.beginPath(); ctx.arc(cx2, cy2, r, 0, Math.PI * 2); ctx.fill();
+        }
+
+        /* Mid vivid-orange body */
+        {
+          const r = cs * 0.46 * (0.8 + eff * 0.2);
+          const cx2 = px + windDx * 0.35, cy2 = py + windDy * 0.35 - cs * 0.04 * eff;
+          const gr = ctx.createRadialGradient(cx2, cy2, 0, cx2, cy2, r);
+          gr.addColorStop(0,   `rgba(255,110,0,${eff * 0.92})`);
+          gr.addColorStop(0.45,`rgba(255,60,0,${eff * 0.62})`);
+          gr.addColorStop(1,   'rgba(220,20,0,0)');
+          ctx.fillStyle = gr;
+          ctx.beginPath(); ctx.arc(cx2, cy2, r, 0, Math.PI * 2); ctx.fill();
+        }
+
+        /* Inner bright-yellow core */
+        {
+          const r = cs * 0.28 * eff;
+          const cx2 = px + windDx * 0.15, cy2 = py + windDy * 0.15 - cs * 0.10 * eff;
+          const gr = ctx.createRadialGradient(cx2, cy2, 0, cx2, cy2, r);
+          gr.addColorStop(0,   `rgba(255,235,90,${eff * 0.97})`);
+          gr.addColorStop(0.35,`rgba(255,175,25,${eff * 0.78})`);
+          gr.addColorStop(1,   'rgba(255,80,0,0)');
+          ctx.fillStyle = gr;
+          ctx.beginPath(); ctx.arc(cx2, cy2, r, 0, Math.PI * 2); ctx.fill();
+        }
+
+        /* White-hot tip (only for intense fire) */
+        if (eff > 0.55) {
+          const r = cs * 0.14 * eff;
+          const cx2 = px + windDx * 0.1, cy2 = py + windDy * 0.1 - cs * 0.18 * eff;
+          const gr = ctx.createRadialGradient(cx2, cy2, 0, cx2, cy2, r);
+          gr.addColorStop(0, `rgba(255,255,230,${eff * 0.95})`);
+          gr.addColorStop(1, 'rgba(255,220,60,0)');
+          ctx.fillStyle = gr;
+          ctx.beginPath(); ctx.arc(cx2, cy2, r, 0, Math.PI * 2); ctx.fill();
+        }
+
+        /* Wind-carried plume tip */
+        if (fire > 0.35) {
+          const r  = cs * 0.30 * eff;
+          const cx2 = px + windDx, cy2 = py + windDy - cs * 0.22 * eff;
+          const gr = ctx.createRadialGradient(cx2, cy2, 0, cx2, cy2, r);
+          gr.addColorStop(0, `rgba(255,165,10,${eff * 0.68})`);
+          gr.addColorStop(1, 'rgba(255,60,0,0)');
+          ctx.fillStyle = gr;
+          ctx.beginPath(); ctx.arc(cx2, cy2, r, 0, Math.PI * 2); ctx.fill();
+        }
+
+        /* Outer visible bloom ring (makes fire pop even in fog) */
+        {
+          const bloomR = cs * (0.85 + eff * 0.35);
+          const bloomGr = ctx.createRadialGradient(px, py, cs * 0.2, px, py, bloomR);
+          bloomGr.addColorStop(0, 'rgba(255,120,0,0)');
+          bloomGr.addColorStop(0.6, `rgba(255,80,0,${eff * 0.22})`);
+          bloomGr.addColorStop(1,   'rgba(200,30,0,0)');
+          ctx.fillStyle = bloomGr;
+          ctx.beginPath(); ctx.arc(px, py, bloomR, 0, Math.PI * 2); ctx.fill();
+        }
+
+        if (fire > 0.45 && Math.random() < 0.09 && embersRef.current.length < 120) {
+          embersRef.current.push(new Ember(px, py, wv[0]));
+        }
+      }
+    }
+
+    /* ── Vision lantern glow around agent ── */
+    const apx = agent_x * cs + cs / 2, apy = agent_y * cs + cs / 2;
+    const lanternR = cs * 3.5;
+    const lanternGr = ctx.createRadialGradient(apx, apy, 0, apx, apy, lanternR);
+    lanternGr.addColorStop(0,   'rgba(255,240,190,0.18)');
+    lanternGr.addColorStop(0.5, 'rgba(255,220,140,0.08)');
+    lanternGr.addColorStop(1,   'rgba(0,0,0,0)');
+    ctx.fillStyle = lanternGr;
+    ctx.fillRect(apx - lanternR, apy - lanternR, lanternR * 2, lanternR * 2);
+
+    /* ── Agent trail ── */
+    const now = timeRef.current;
+    if (
+      trailRef.current.length === 0 ||
+      Math.abs(trailRef.current[0].x - apx) > 1 ||
+      Math.abs(trailRef.current[0].y - apy) > 1
+    ) {
+      trailRef.current.unshift({ x: apx, y: apy, t: now });
+    }
+    if (trailRef.current.length > 20) trailRef.current.pop();
+
+    trailRef.current.forEach((p, i) => {
+      const alpha = (1 - i / trailRef.current.length) * 0.70;
+      ctx.fillStyle = `rgba(2,132,199,${alpha})`;
+      ctx.beginPath();
+      ctx.arc(p.x, p.y, cs * 0.12 * (1 - i / 22), 0, Math.PI * 2);
+      ctx.fill();
+    });
+
+    /* ── Agent rendering ── */
+    const theme =
+      agent_health >= 60 ? AGENT_THEMES.healthy  :
+      agent_health >= 30 ? AGENT_THEMES.moderate :
+      agent_health >  0  ? AGENT_THEMES.low       :
+                           AGENT_THEMES.critical;
+
+    const pulse = 0.85 + 0.15 * Math.sin(t * 4);
+    const ringR = cs * 0.48;
+
+    /* pulsing gold aura */
+    const auraR = ringR * (1.5 + 0.2 * pulse);
+    const auraGr = ctx.createRadialGradient(apx, apy, ringR * 0.7, apx, apy, auraR);
+    auraGr.addColorStop(0, `rgba(251,191,36,${0.28 * pulse})`);
+    auraGr.addColorStop(1, 'rgba(251,191,36,0)');
+    ctx.fillStyle = auraGr;
+    ctx.beginPath(); ctx.arc(apx, apy, auraR, 0, Math.PI * 2); ctx.fill();
+
+    /* ground shadow */
+    ctx.save();
+    ctx.globalAlpha = 0.22;
+    const shadowGr = ctx.createRadialGradient(apx, apy + cs * 0.32, 0, apx, apy + cs * 0.32, cs * 0.38);
+    shadowGr.addColorStop(0, 'rgba(0,0,0,0.6)');
+    shadowGr.addColorStop(1, 'rgba(0,0,0,0)');
+    ctx.fillStyle = shadowGr;
+    ctx.beginPath(); ctx.ellipse(apx, apy + cs * 0.32, cs * 0.38, cs * 0.14, 0, 0, Math.PI * 2); ctx.fill();
+    ctx.restore();
+
+    /* Minecraft character */
+    drawMinecraftAgent(ctx, apx, apy, cs, theme);
+
+    /* health arc ring — gold ring + colored fill */
+    const hRatio = Math.max(0, Math.min(1, agent_health / 100));
+    /* ring track */
+    ctx.beginPath();
+    ctx.arc(apx, apy, ringR, 0, Math.PI * 2);
+    ctx.strokeStyle = 'rgba(0,0,0,0.12)';
+    ctx.lineWidth = 4;
+    ctx.lineCap = 'round';
+    ctx.stroke();
+    /* gold base ring */
+    ctx.beginPath();
+    ctx.arc(apx, apy, ringR, 0, Math.PI * 2);
+    ctx.strokeStyle = 'rgba(251,191,36,0.25)';
+    ctx.lineWidth = 3.5;
+    ctx.stroke();
+    /* health fill */
+    ctx.beginPath();
+    ctx.arc(apx, apy, ringR, -Math.PI / 2, -Math.PI / 2 + hRatio * Math.PI * 2);
+    ctx.strokeStyle = theme.ring;
+    ctx.lineWidth = 3.5;
+    ctx.lineCap = 'round';
+    ctx.stroke();
+    /* ring glow */
+    ctx.beginPath();
+    ctx.arc(apx, apy, ringR, -Math.PI / 2, -Math.PI / 2 + hRatio * Math.PI * 2);
+    ctx.strokeStyle = theme.ringGlow;
+    ctx.lineWidth = 6;
+    ctx.stroke();
+
+    /* move flash */
+    if (agentMoveFlash > 0) {
+      const fa = agentMoveFlash / 18;
+      ctx.strokeStyle = `rgba(255,255,255,${fa * 0.8})`;
+      ctx.lineWidth = 3;
+      ctx.beginPath();
+      ctx.arc(apx, apy, ringR * (1.8 + (1 - fa) * 0.6), 0, Math.PI * 2);
+      ctx.stroke();
+    }
+
+    /* ── Embers ── */
+    for (let i = embersRef.current.length - 1; i >= 0; i--) {
+      const e = embersRef.current[i];
+      e.update();
+      if (e.life <= 0) { embersRef.current.splice(i, 1); continue; }
+      ctx.fillStyle = `rgba(255,${Math.floor(80 + 175 * e.life)},0,${e.life})`;
+      ctx.beginPath(); ctx.arc(e.x, e.y, e.size * e.life, 0, Math.PI * 2); ctx.fill();
+    }
+
+    rafRef.current = requestAnimationFrame(animate);
+  };
+
+  useEffect(() => {
+    rafRef.current = requestAnimationFrame(animate);
+    return () => { if (rafRef.current) cancelAnimationFrame(rafRef.current); };
+  // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [observation, agentMoveFlash]);
+
+  const W = observation?.map_state.grid_w ?? 16;
+  const H = observation?.map_state.grid_h ?? 16;
+
+  return (
+    <canvas
+      ref={canvasRef}
+      id="map-canvas"
+      width={W * CS}
+      height={H * CS}
+    />
+  );
+};
+
+export default Map2D;

frontend/src/components/StatusCard.tsx

@@ -0,0 +1,26 @@
+import React from 'react';
+
+interface StatusRowProps {
+  label: string;
+  value: string | number;
+  className?: string;
+}
+
+export const StatusRow: React.FC<StatusRowProps> = ({ label, value, className = '' }) => (
+  <div className="srow">
+    <span>{label}</span>
+    <span className={`sv ${className}`}>{value}</span>
+  </div>
+);
+
+interface StatusCardProps {
+  title: string;
+  children: React.ReactNode;
+}
+
+export const StatusCard: React.FC<StatusCardProps> = ({ title, children }) => (
+  <div className="card">
+    <div className="card-title">{title}</div>
+    {children}
+  </div>
+);

frontend/src/index.css

@@ -0,0 +1 @@
+/* index.css cleared to use App.css exclusively for project styling */

frontend/src/main.tsx

@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.tsx'
+
+createRoot(document.getElementById('root')!).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)

frontend/src/types.ts

@@ -0,0 +1,112 @@
+export interface SceneLabels {
+  agent: {
+    x: number;
+    y: number;
+    health: number;
+    health_status: string;
+    alive: boolean;
+    evacuated: boolean;
+    location: string;
+    smoke_level: string;
+    fire_visible: boolean;
+    fire_direction: string | null;
+    last_action_feedback: string;
+  };
+  episode: {
+    id: string;
+    step: number;
+    max_steps: number;
+    template: string;
+    difficulty: string;
+    wind_dir: string;
+    fire_spread_rate: number;
+    humidity: number;
+    fire_sources: number;
+  };
+  map: {
+    width: number;
+    height: number;
+    exit_positions: [number, number][];
+    door_registry: Record<string, [number, number]>;
+  };
+  surroundings: {
+    visible_objects: {
+      id: string;
+      type: string;
+      relative_pos: string;
+      state: string;
+    }[];
+    blocked_exit_ids: string[];
+    audible_signals: string[];
+    available_actions: string[];
+  };
+}
+
+export interface SceneGraph {
+  channels: string[];
+  channel_info: Record<string, string>;
+  width: number;
+  height: number;
+  grid: number[][][]; // grid[y][x] = [cell_type, fire, smoke, is_agent, is_visible]
+}
+
+export interface SceneResponse {
+  labels: SceneLabels;
+  graph: SceneGraph;
+}
+
+export interface Observation {
+  map_state: {
+    cell_grid: number[];
+    fire_grid: number[];
+    smoke_grid: number[];
+    agent_x: number;
+    agent_y: number;
+    visible_cells: [number, number][];
+    door_registry: Record<string, [number, number]>;
+    exit_positions: [number, number][];
+    step_count: number;
+    max_steps: number;
+    grid_w: number;
+    grid_h: number;
+    template_name: string;
+  };
+  agent_health: number;
+  location_label: string;
+  smoke_level: string;
+  wind_dir: string;
+  fire_visible: boolean;
+  fire_direction: string | null;
+  last_action_feedback: string;
+  narrative: string;
+  reward?: number;
+  done?: boolean;
+  metadata?: {
+    fire_sources: number;
+    fire_spread_rate: number;
+    humidity: number;
+    difficulty: string;
+  };
+}
+
+export interface Door {
+  id: string;
+  x: number;
+  y: number;
+  state: 'open' | 'closed' | 'failed';
+}
+
+export interface StaffMember {
+  id: string;
+  x: number;
+  y: number;
+  phase: number;
+  mood: 'calm' | 'panicked';
+}
+
+export interface ApiReport {
+  call_type: string;
+  request: any;
+  response: any;
+  meta: string;
+}

frontend/tsconfig.app.json

@@ -0,0 +1,25 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+    "target": "es2023",
+    "lib": ["ES2023", "DOM"],
+    "module": "esnext",
+    "types": ["vite/client"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+    "jsx": "react-jsx",
+
+    /* Linting */
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true
+  },
+  "include": ["src"]
+}

frontend/tsconfig.json

@@ -0,0 +1,7 @@
+{
+  "files": [],
+  "references": [
+    { "path": "./tsconfig.app.json" },
+    { "path": "./tsconfig.node.json" }
+  ]
+}

frontend/tsconfig.node.json

@@ -0,0 +1,24 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+    "target": "es2023",
+    "lib": ["ES2023"],
+    "module": "esnext",
+    "types": ["node"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+
+    /* Linting */
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true
+  },
+  "include": ["vite.config.ts"]
+}

frontend/vite.config.ts

@@ -0,0 +1,20 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+import path from 'path'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+  build: {
+    outDir: path.resolve(__dirname, '../server/static'),
+    emptyOutDir: true,
+  },
+  server: {
+    proxy: {
+      '/state': 'http://127.0.0.1:8000',
+      '/reset': 'http://127.0.0.1:8000',
+      '/step': 'http://127.0.0.1:8000',
+      '/scene': 'http://127.0.0.1:8000',
+    }
+  }
+})

openenv_pyre_env.egg-info/PKG-INFO

@@ -13,3 +13,16 @@ Requires-Dist: langchain-openai>=1.2.1
 Provides-Extra: dev
 Requires-Dist: pytest>=8.0.0; extra == "dev"
 Requires-Dist: pytest-cov>=4.0.0; extra == "dev"
+Provides-Extra: train
+Requires-Dist: datasets>=4.8.4; extra == "train"
+Requires-Dist: peft>=0.15.2; extra == "train"
+Requires-Dist: tensorboard>=2.20.0; extra == "train"
+Requires-Dist: torch>=2.9.0; extra == "train"
+Requires-Dist: transformers>=4.57.6; extra == "train"
+Requires-Dist: trl>=1.2.0; extra == "train"
+Requires-Dist: tornado>=6.5.5; extra == "train"
+Requires-Dist: vllm<=0.18.0,>=0.11.0; extra == "train"
+Requires-Dist: flashinfer-python==0.6.3; extra == "train"
+Requires-Dist: flashinfer-cubin==0.6.3; extra == "train"
+Requires-Dist: jupyter>=1.1.1; extra == "train"
+Requires-Dist: flash-attn>=2.7.0; extra == "train"

openenv_pyre_env.egg-info/SOURCES.txt

@@ -4,6 +4,7 @@ client.py
 evals.py
 models.py
 pyproject.toml
+train_grpo_openenv.py
 ./__init__.py
 ./client.py
 ./evals.py
@@ -20,5 +21,6 @@ server/app.py
 server/fire_sim.py
 server/floor_plan.py
 server/narrative.py
+server/npc_model.py
 server/pyre_env_environment.py
 server/rubrics.py

openenv_pyre_env.egg-info/requires.txt

@@ -9,3 +9,17 @@ langchain-openai>=1.2.1
 [dev]
 pytest>=8.0.0
 pytest-cov>=4.0.0
+
+[train]
+datasets>=4.8.4
+peft>=0.15.2
+tensorboard>=2.20.0
+torch>=2.9.0
+transformers>=4.57.6
+trl>=1.2.0
+tornado>=6.5.5
+vllm<=0.18.0,>=0.11.0
+flashinfer-python==0.6.3
+flashinfer-cubin==0.6.3
+jupyter>=1.1.1
+flash-attn>=2.7.0

outputs/20260425_154907_Qwen-Qwen3-06B/error.txt

@@ -0,0 +1,57 @@
+warning: Failed to uninstall package at .venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info due to missing `RECORD` file. Installation may result in an incomplete environment.
+Uninstalled 2 packages in 6.94s
+warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
+         If the cache and target directories are on different filesystems, hardlinking may not be supported.
+         If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
+Installed 1 package in 1.15s
+[2026-04-25 16:01:06] INFO train_grpo_openenv.py:567: Model: Qwen/Qwen3-0.6B
+[2026-04-25 16:01:06] INFO train_grpo_openenv.py:573: Dataset: 1000 prompts
+[2026-04-25 16:01:07] INFO train_grpo_openenv.py:608: Output: ./outputs/20260425_154907_Qwen-Qwen3-06B | vLLM mode: colocate
+`torch_dtype` is deprecated! Use `dtype` instead!
+Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
+Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
+<frozen importlib._bootstrap_external>:1301: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
+<frozen importlib._bootstrap_external>:1301: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
+
+
+
+
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/train_grpo_openenv.py", line 611, in <module>
+[rank0]:     trainer = GRPOTrainer(
+[rank0]:               ^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/trl/trainer/grpo_trainer.py", line 750, in __init__
+[rank0]:     self.vllm_generation = VLLMGeneration(
+[rank0]:                            ^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/trl/generation/vllm_generation.py", line 282, in __init__
+[rank0]:     self._init_vllm()
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/trl/generation/vllm_generation.py", line 342, in _init_vllm
+[rank0]:     self.llm = LLM(
+[rank0]:                ^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/entrypoints/llm.py", line 346, in __init__
+[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
+[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/engine/llm_engine.py", line 174, in from_engine_args
+[rank0]:     return cls(
+[rank0]:            ^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/engine/llm_engine.py", line 108, in __init__
+[rank0]:     self.engine_core = EngineCoreClient.make_client(
+[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 97, in make_client
+[rank0]:     return InprocClient(vllm_config, executor_class, log_stats)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 277, in __init__
+[rank0]:     self.engine_core = EngineCore(*args, **kwargs)
+[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 113, in __init__
+[rank0]:     num_gpu_blocks, num_cpu_blocks, kv_cache_config = self._initialize_kv_caches(
+[rank0]:                                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 259, in _initialize_kv_caches
+[rank0]:     kv_cache_configs = get_kv_cache_configs(
+[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/core/kv_cache_utils.py", line 1516, in get_kv_cache_configs
+[rank0]:     _check_enough_kv_cache_memory(
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/vllm/v1/core/kv_cache_utils.py", line 634, in _check_enough_kv_cache_memory
+[rank0]:     raise ValueError(
+[rank0]: ValueError: To serve at least one request with the models's max seq len (8192), (0.88 GiB KV cache is needed, which is larger than the available KV cache memory (0.12 GiB). Based on the available memory, the estimated maximum model length is 1104. Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine. See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ for more details.
+[rank0]:[W425 16:05:53.916115210 ProcessGroupNCCL.cpp:1524] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

outputs/20260425_154907_Qwen-Qwen3-06B/output.txt

@@ -0,0 +1,46 @@
+Helper functions defined.
+Rollout functions defined.
+Reward functions: evacuated, step_efficiency, format, efficiency
+
+------------------------------------------------------------
+Sender: LSF System <lsfadmin@cccxc590>
+Subject: Job 1281121: <20260425_154907_Qwen-Qwen3-06B> in cluster <CCCFP15Cluster> Exited
+
+Job <20260425_154907_Qwen-Qwen3-06B> was submitted from host <ccc-login3> by user <kirushi> in cluster <CCCFP15Cluster> at Sat Apr 25 15:50:13 2026
+Job was executed on host(s) <cccxc590>, in queue <normal>, as user <kirushi> in cluster <CCCFP15Cluster> at Sat Apr 25 15:49:50 2026
+</u/kirushi> was used as the home directory.
+</dccstor/kirushikesh/personal-projects/openenv-pyre> was used as the working directory.
+Started at Sat Apr 25 15:49:50 2026
+Terminated at Sat Apr 25 16:05:56 2026
+Results reported at Sat Apr 25 16:05:56 2026
+
+Your job looked like:
+
+------------------------------------------------------------
+# LSBATCH: User input
+/u/kirushi/.local/bin/uv run python train_grpo_openenv.py --model-id Qwen/Qwen3-0.6B --dataset-size 1000 --output-dir ./outputs/20260425_154907_Qwen-Qwen3-06B --report-to tensorboard --seed 42
+------------------------------------------------------------
+
+Exited with exit code 1.
+
+Resource usage summary:
+
+    CPU time :                                   374.61 sec.
+    Max Memory :                                 3 GB
+    Average Memory :                             1.39 GB
+    Total Requested Memory :                     -
+    Delta Memory :                               -
+    Max Swap :                                   -
+    Max Processes :                              5
+    Max Threads :                                494
+    Run time :                                   966 sec.
+    Turnaround time :                            943 sec.
+
+The output (if any) is above this job summary.
+
+
+
+PS:
+
+Read file <./outputs/20260425_154907_Qwen-Qwen3-06B/error.txt> for stderr output of this job.
+

outputs/20260425_154915_Qwen-Qwen3-17B/README.md

@@ -0,0 +1,67 @@
+---
+base_model: Qwen/Qwen3-1.7B
+library_name: transformers
+model_name: 20260425_154915_Qwen-Qwen3-17B
+tags:
+- generated_from_trainer
+- trl
+- grpo
+licence: license
+---
+
+# Model Card for 20260425_154915_Qwen-Qwen3-17B
+
+This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+ 
+
+
+
+This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
+
+### Framework versions
+
+- TRL: 1.2.0
+- Transformers: 4.57.6
+- Pytorch: 2.9.1
+- Datasets: 4.8.4
+- Tokenizers: 0.22.2
+
+## Citations
+
+Cite GRPO as:
+
+```bibtex
+@article{shao2024deepseekmath,
+    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
+    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
+    year         = 2024,
+    eprint       = {arXiv:2402.03300},
+}
+```
+
+Cite TRL as:
+    
+```bibtex
+@software{vonwerra2020trl,
+  title   = {{TRL: Transformers Reinforcement Learning}},
+  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
+  license = {Apache-2.0},
+  url     = {https://github.com/huggingface/trl},
+  year    = {2020}
+}
+```

outputs/20260425_154915_Qwen-Qwen3-17B/checkpoint-10/config.json

@@ -0,0 +1,60 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

outputs/20260425_154915_Qwen-Qwen3-17B/checkpoint-10/generation_config.json

@@ -0,0 +1,12 @@
+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}

outputs/20260425_154915_Qwen-Qwen3-17B/checkpoint-10/model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69a3cc129138e8190c7a058ddd4346f9c540ff3b4f110490e8a796ffc4457942
+size 5242880

outputs/20260425_154915_Qwen-Qwen3-17B/checkpoint-10/model-00002-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a66c56b9b128725068ad1913324cd9d69e411d41c81e627caa41d68c7a5db527
+size 5242880

outputs/20260425_154915_Qwen-Qwen3-17B/error.txt

@@ -0,0 +1,220 @@
+warning: Failed to uninstall package at .venv/lib/python3.12/site-packages/numpy-2.4.4.dist-info due to missing `RECORD` file. Installation may result in an incomplete environment.
+Uninstalled 2 packages in 1.65s
+warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
+         If the cache and target directories are on different filesystems, hardlinking may not be supported.
+         If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
+Installed 1 package in 1.24s
+[2026-04-25 16:01:06] INFO train_grpo_openenv.py:567: Model: Qwen/Qwen3-1.7B
+[2026-04-25 16:01:06] INFO train_grpo_openenv.py:573: Dataset: 1000 prompts
+[2026-04-25 16:01:07] INFO train_grpo_openenv.py:608: Output: ./outputs/20260425_154915_Qwen-Qwen3-17B | vLLM mode: colocate
+`torch_dtype` is deprecated! Use `dtype` instead!
+Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
+Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
+
+<frozen importlib._bootstrap_external>:1301: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
+<frozen importlib._bootstrap_external>:1301: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
+
+
+
+
+
+
+[2026-04-25 16:05:55] INFO train_grpo_openenv.py:630: GPU: NVIDIA A100-SXM4-80GB — 79.251 GB total, 25.477 GB reserved
+The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
+
  0%|          | 0/500 [00:00<?, ?it/s][2026-04-25 16:05:56] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=794772
+[2026-04-25 16:06:33] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.030 | steps=15 | fmt=87%
+[2026-04-25 16:06:33] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=42450
+[2026-04-25 16:07:07] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.040 | steps=14 | fmt=86%
+[2026-04-25 16:07:07] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=536110
+[2026-04-25 16:07:41] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.021 | steps=13 | fmt=92%
+[2026-04-25 16:07:41] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=962838
+[2026-04-25 16:08:15] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.009 | steps=13 | fmt=85%
+[2026-04-25 16:08:15] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=318046
+[2026-04-25 16:08:48] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.010 | steps=11 | fmt=82%
+[2026-04-25 16:08:48] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=375441
+[2026-04-25 16:09:24] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.014 | steps=14 | fmt=86%
+[2026-04-25 16:09:24] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=952225
+[2026-04-25 16:09:58] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.023 | steps=14 | fmt=86%
+[2026-04-25 16:09:58] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=146039
+[2026-04-25 16:10:06] INFO train_grpo_openenv.py:466:   → EVACUATED | health=100.0 | mean_step=2.822 | steps=6 | fmt=100%
+Casting fp32 inputs back to torch.bfloat16 for flash-attn compatibility.
+Could not estimate the number of tokens of the input, floating-point operations will not be computed
+
  0%|          | 1/500 [04:42<39:13:06, 282.94s/it]
                                                   
  0%|          | 1/500 [04:43<39:13:06, 282.94s/it][2026-04-25 16:10:39] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=792518
+[2026-04-25 16:11:15] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.002 | steps=15 | fmt=80%
+[2026-04-25 16:11:15] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=838234
+[2026-04-25 16:11:49] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.019 | steps=15 | fmt=93%
+[2026-04-25 16:11:49] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=953938
+[2026-04-25 16:12:26] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.001 | steps=16 | fmt=88%
+[2026-04-25 16:12:26] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=739426
+[2026-04-25 16:13:00] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.017 | steps=16 | fmt=94%
+[2026-04-25 16:13:00] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=945989
+[2026-04-25 16:13:34] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.019 | steps=14 | fmt=93%
+[2026-04-25 16:13:34] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=103560
+[2026-04-25 16:14:07] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.018 | steps=16 | fmt=100%
+[2026-04-25 16:14:07] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=942500
+[2026-04-25 16:14:42] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.015 | steps=18 | fmt=94%
+[2026-04-25 16:14:42] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=346236
+[2026-04-25 16:15:15] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.004 | steps=16 | fmt=88%
+
  0%|          | 2/500 [09:56<41:39:36, 301.16s/it]
                                                   
  0%|          | 2/500 [09:57<41:39:36, 301.16s/it][2026-04-25 16:15:53] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=105592
+[2026-04-25 16:16:29] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.025 | steps=16 | fmt=88%
+[2026-04-25 16:16:29] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=331556
+[2026-04-25 16:17:03] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.017 | steps=13 | fmt=85%
+[2026-04-25 16:17:03] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=957361
+[2026-04-25 16:17:40] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.059 | steps=12 | fmt=58%
+[2026-04-25 16:17:40] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=579363
+[2026-04-25 16:18:16] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.017 | steps=15 | fmt=100%
+[2026-04-25 16:18:16] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=907343
+[2026-04-25 16:18:55] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.011 | steps=15 | fmt=93%
+[2026-04-25 16:18:55] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=65304
+[2026-04-25 16:19:30] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.001 | steps=15 | fmt=93%
+[2026-04-25 16:19:30] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=575352
+[2026-04-25 16:20:06] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.035 | steps=15 | fmt=93%
+[2026-04-25 16:20:06] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=97802
+[2026-04-25 16:20:43] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.013 | steps=14 | fmt=100%
+
  1%|          | 3/500 [15:20<42:57:56, 311.22s/it]
                                                   
  1%|          | 3/500 [15:20<42:57:56, 311.22s/it][2026-04-25 16:21:16] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=418196
+[2026-04-25 16:21:25] INFO train_grpo_openenv.py:466:   → EVACUATED | health=100.0 | mean_step=2.803 | steps=6 | fmt=100%
+[2026-04-25 16:21:25] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=823182
+[2026-04-25 16:22:01] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.045 | steps=15 | fmt=100%
+[2026-04-25 16:22:01] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=1198
+[2026-04-25 16:22:37] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.024 | steps=14 | fmt=93%
+[2026-04-25 16:22:37] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=868287
+[2026-04-25 16:23:13] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.028 | steps=12 | fmt=92%
+[2026-04-25 16:23:13] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=255759
+[2026-04-25 16:23:47] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.010 | steps=15 | fmt=87%
+[2026-04-25 16:23:47] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=737822
+[2026-04-25 16:24:22] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.032 | steps=13 | fmt=85%
+[2026-04-25 16:24:22] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=200348
+[2026-04-25 16:25:00] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.027 | steps=14 | fmt=86%
+[2026-04-25 16:25:00] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=232473
+[2026-04-25 16:25:36] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.011 | steps=15 | fmt=100%
+
  1%|          | 4/500 [20:11<41:48:51, 303.49s/it]
                                                   
  1%|          | 4/500 [20:11<41:48:51, 303.49s/it][2026-04-25 16:26:07] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=149416
+[2026-04-25 16:26:43] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.028 | steps=16 | fmt=88%
+[2026-04-25 16:26:43] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=469730
+[2026-04-25 16:27:19] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.007 | steps=15 | fmt=100%
+[2026-04-25 16:27:19] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=335601
+[2026-04-25 16:27:54] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.005 | steps=14 | fmt=86%
+[2026-04-25 16:27:54] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=978147
+[2026-04-25 16:28:30] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.015 | steps=15 | fmt=87%
+[2026-04-25 16:28:30] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=316089
+[2026-04-25 16:29:06] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.016 | steps=17 | fmt=94%
+[2026-04-25 16:29:06] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=740883
+[2026-04-25 16:29:43] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.005 | steps=15 | fmt=87%
+[2026-04-25 16:29:43] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=348914
+[2026-04-25 16:30:19] INFO train_grpo_openenv.py:466:   → failed | health=50.0 | mean_step=-0.159 | steps=13 | fmt=85%
+[2026-04-25 16:30:19] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=566528
+[2026-04-25 16:30:54] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.001 | steps=13 | fmt=77%
+
  1%|          | 5/500 [25:32<42:35:25, 309.75s/it]
                                                   
  1%|          | 5/500 [25:32<42:35:25, 309.75s/it][2026-04-25 16:31:28] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=838260
+[2026-04-25 16:32:05] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.004 | steps=12 | fmt=83%
+[2026-04-25 16:32:05] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=616161
+[2026-04-25 16:32:40] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.038 | steps=12 | fmt=75%
+[2026-04-25 16:32:40] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=96083
+[2026-04-25 16:32:55] INFO train_grpo_openenv.py:466:   → EVACUATED | health=100.0 | mean_step=1.823 | steps=9 | fmt=100%
+[2026-04-25 16:32:55] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=403598
+[2026-04-25 16:33:30] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.044 | steps=14 | fmt=79%
+[2026-04-25 16:33:30] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=253867
+[2026-04-25 16:34:08] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.018 | steps=13 | fmt=77%
+[2026-04-25 16:34:08] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=198591
+[2026-04-25 16:34:43] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.009 | steps=13 | fmt=85%
+[2026-04-25 16:34:43] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=34574
+[2026-04-25 16:35:19] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.027 | steps=15 | fmt=93%
+[2026-04-25 16:35:19] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=688557
+[2026-04-25 16:35:53] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.034 | steps=14 | fmt=93%
+
  1%|          | 6/500 [30:28<41:51:02, 304.98s/it]
                                                   
  1%|          | 6/500 [30:28<41:51:02, 304.98s/it][2026-04-25 16:36:24] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=72441
+[2026-04-25 16:36:57] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.019 | steps=14 | fmt=93%
+[2026-04-25 16:36:57] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=794405
+[2026-04-25 16:37:32] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.000 | steps=15 | fmt=93%
+[2026-04-25 16:37:32] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=156814
+[2026-04-25 16:38:07] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.004 | steps=14 | fmt=71%
+[2026-04-25 16:38:07] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=883383
+[2026-04-25 16:38:44] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.005 | steps=13 | fmt=92%
+[2026-04-25 16:38:44] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=733293
+[2026-04-25 16:39:16] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.058 | steps=12 | fmt=75%
+[2026-04-25 16:39:16] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=566860
+[2026-04-25 16:39:51] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.031 | steps=13 | fmt=92%
+[2026-04-25 16:39:51] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=878565
+[2026-04-25 16:40:26] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.045 | steps=13 | fmt=85%
+[2026-04-25 16:40:26] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=289023
+[2026-04-25 16:41:01] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.032 | steps=13 | fmt=69%
+
  1%|▏         | 7/500 [35:36<41:54:16, 306.00s/it]
                                                   
  1%|▏         | 7/500 [35:36<41:54:16, 306.00s/it][2026-04-25 16:41:32] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=246941
+[2026-04-25 16:42:07] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.031 | steps=13 | fmt=77%
+[2026-04-25 16:42:07] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=938516
+[2026-04-25 16:42:43] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.034 | steps=16 | fmt=94%
+[2026-04-25 16:42:43] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=865351
+[2026-04-25 16:43:16] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.056 | steps=13 | fmt=77%
+[2026-04-25 16:43:16] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=607854
+[2026-04-25 16:43:49] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.019 | steps=15 | fmt=93%
+[2026-04-25 16:43:49] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=516586
+[2026-04-25 16:44:25] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.031 | steps=15 | fmt=87%
+[2026-04-25 16:44:25] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=734239
+[2026-04-25 16:45:00] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.016 | steps=14 | fmt=93%
+[2026-04-25 16:45:00] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=831861
+[2026-04-25 16:45:34] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.017 | steps=15 | fmt=100%
+[2026-04-25 16:45:34] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=340079
+[2026-04-25 16:46:08] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.007 | steps=13 | fmt=77%
+
  2%|▏         | 8/500 [40:45<41:57:45, 307.04s/it]
                                                   
  2%|▏         | 8/500 [40:45<41:57:45, 307.04s/it][2026-04-25 16:46:42] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=510073
+[2026-04-25 16:47:17] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.033 | steps=13 | fmt=100%
+[2026-04-25 16:47:17] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=351556
+[2026-04-25 16:47:54] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.004 | steps=13 | fmt=77%
+[2026-04-25 16:47:54] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=254841
+[2026-04-25 16:48:29] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.017 | steps=18 | fmt=94%
+[2026-04-25 16:48:29] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=284203
+[2026-04-25 16:48:36] INFO train_grpo_openenv.py:466:   → EVACUATED | health=100.0 | mean_step=4.190 | steps=4 | fmt=100%
+[2026-04-25 16:48:36] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=231169
+[2026-04-25 16:49:10] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.021 | steps=14 | fmt=93%
+[2026-04-25 16:49:10] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=178763
+[2026-04-25 16:49:44] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.021 | steps=15 | fmt=100%
+[2026-04-25 16:49:44] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=855546
+[2026-04-25 16:50:19] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.014 | steps=13 | fmt=92%
+[2026-04-25 16:50:19] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=821159
+[2026-04-25 16:50:55] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.022 | steps=14 | fmt=86%
+
  2%|▏         | 9/500 [45:33<41:03:58, 301.10s/it]
                                                   
  2%|▏         | 9/500 [45:33<41:03:58, 301.10s/it][2026-04-25 16:51:29] INFO train_grpo_openenv.py:444: Episode 1 | difficulty=easy | seed=731560
+[2026-04-25 16:52:07] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.021 | steps=13 | fmt=85%
+[2026-04-25 16:52:07] INFO train_grpo_openenv.py:444: Episode 2 | difficulty=easy | seed=856812
+[2026-04-25 16:52:40] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.025 | steps=13 | fmt=69%
+[2026-04-25 16:52:40] INFO train_grpo_openenv.py:444: Episode 3 | difficulty=easy | seed=954220
+[2026-04-25 16:53:15] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.001 | steps=13 | fmt=77%
+[2026-04-25 16:53:15] INFO train_grpo_openenv.py:444: Episode 4 | difficulty=easy | seed=631421
+[2026-04-25 16:53:50] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.021 | steps=11 | fmt=55%
+[2026-04-25 16:53:50] INFO train_grpo_openenv.py:444: Episode 5 | difficulty=easy | seed=27993
+[2026-04-25 16:54:25] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.015 | steps=19 | fmt=100%
+[2026-04-25 16:54:25] INFO train_grpo_openenv.py:444: Episode 6 | difficulty=easy | seed=197678
+[2026-04-25 16:55:01] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=0.019 | steps=14 | fmt=93%
+[2026-04-25 16:55:01] INFO train_grpo_openenv.py:444: Episode 7 | difficulty=easy | seed=603930
+[2026-04-25 16:55:35] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.026 | steps=11 | fmt=73%
+[2026-04-25 16:55:35] INFO train_grpo_openenv.py:444: Episode 8 | difficulty=easy | seed=95978
+[2026-04-25 16:56:11] INFO train_grpo_openenv.py:466:   → failed | health=100.0 | mean_step=-0.045 | steps=12 | fmt=83%
+
  2%|▏         | 10/500 [50:50<41:38:33, 305.95s/it]
                                                    
  2%|▏         | 10/500 [50:50<41:38:33, 305.95s/it][rank0]: Traceback (most recent call last):
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/train_grpo_openenv.py", line 633, in <module>
+[rank0]:     trainer_stats = trainer.train()
+[rank0]:                     ^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/transformers/trainer.py", line 2325, in train
+[rank0]:     return inner_training_loop(
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/transformers/trainer.py", line 2756, in _inner_training_loop
+[rank0]:     self._maybe_log_save_evaluate(
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/transformers/trainer.py", line 3228, in _maybe_log_save_evaluate
+[rank0]:     self._save_checkpoint(model, trial)
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/trl/trainer/grpo_trainer.py", line 2719, in _save_checkpoint
+[rank0]:     super()._save_checkpoint(model, trial)
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/transformers/trainer.py", line 3325, in _save_checkpoint
+[rank0]:     self.save_model(output_dir, _internal_call=True)
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/transformers/trainer.py", line 4227, in save_model
+[rank0]:     self._save(output_dir)
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/transformers/trainer.py", line 4331, in _save
+[rank0]:     self.model.save_pretrained(
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 4173, in save_pretrained
+[rank0]:     safe_save_file(shard, os.path.join(save_directory, shard_file), metadata=metadata)
+[rank0]:   File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/safetensors/torch.py", line 307, in save_file
+[rank0]:     serialize_file(_flatten(tensors), filename, metadata=metadata)
+[rank0]: safetensors_rust.SafetensorError: Error while serializing: I/O error: Disk quota exceeded (os error 122)
+Exception ignored in: <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>
+OSError: [Errno 122] Disk quota exceeded
+[rank0]:[W425 16:56:59.402981600 ProcessGroupNCCL.cpp:1524] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+Exception ignored in: <function tqdm.__del__ at 0x14992c829f80>
+Traceback (most recent call last):
+  File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/tqdm/std.py", line 1148, in __del__
+  File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/tqdm/std.py", line 1302, in close
+  File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/tqdm/std.py", line 1495, in display
+  File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/tqdm/std.py", line 1151, in __str__
+  File "/dccstor/kirushikesh/personal-projects/openenv-pyre/.venv/lib/python3.12/site-packages/tqdm/std.py", line 546, in format_meter
+AttributeError: 'NoneType' object has no attribute 'format_interval'

outputs/20260425_154915_Qwen-Qwen3-17B/output.txt

@@ -0,0 +1,46 @@
+Helper functions defined.
+Rollout functions defined.
+Reward functions: evacuated, step_efficiency, format, efficiency
+
+------------------------------------------------------------
+Sender: LSF System <lsfadmin@cccxc590>
+Subject: Job 1281122: <20260425_154915_Qwen-Qwen3-17B> in cluster <CCCFP15Cluster> Exited
+
+Job <20260425_154915_Qwen-Qwen3-17B> was submitted from host <ccc-login3> by user <kirushi> in cluster <CCCFP15Cluster> at Sat Apr 25 15:50:20 2026
+Job was executed on host(s) <cccxc590>, in queue <normal>, as user <kirushi> in cluster <CCCFP15Cluster> at Sat Apr 25 15:49:58 2026
+</u/kirushi> was used as the home directory.
+</dccstor/kirushikesh/personal-projects/openenv-pyre> was used as the working directory.
+Started at Sat Apr 25 15:49:58 2026
+Terminated at Sat Apr 25 16:57:00 2026
+Results reported at Sat Apr 25 16:57:00 2026
+
+Your job looked like:
+
+------------------------------------------------------------
+# LSBATCH: User input
+/u/kirushi/.local/bin/uv run python train_grpo_openenv.py --model-id Qwen/Qwen3-1.7B --dataset-size 1000 --output-dir ./outputs/20260425_154915_Qwen-Qwen3-17B --report-to tensorboard --seed 42
+------------------------------------------------------------
+
+Exited with exit code 120.
+
+Resource usage summary:
+
+    CPU time :                                   3999.24 sec.
+    Max Memory :                                 8 GB
+    Average Memory :                             2.66 GB
+    Total Requested Memory :                     -
+    Delta Memory :                               -
+    Max Swap :                                   -
+    Max Processes :                              5
+    Max Threads :                                625
+    Run time :                                   4022 sec.
+    Turnaround time :                            4000 sec.
+
+The output (if any) is above this job summary.
+
+
+
+PS:
+
+Read file <./outputs/20260425_154915_Qwen-Qwen3-17B/error.txt> for stderr output of this job.
+

outputs/20260425_154915_Qwen-Qwen3-17B/runs/Apr25_16-01-06_cccxc590/events.out.tfevents.1777147555.cccxc590.2920434.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c8afb63bb7a46085a82d14e6a8f0c988a3f2f3c45d8969a4294f653573f645b
+size 31316

pyproject.toml

@@ -28,6 +28,31 @@ dev = [
     "pytest>=8.0.0",
     "pytest-cov>=4.0.0",
 ]
+train = [
+    "datasets>=4.8.4",
+    "peft>=0.15.2",
+    "tensorboard>=2.20.0",
+    "torch>=2.9.0",
+    "transformers>=4.57.6",
+    "trl>=1.2.0",
+    "tornado>=6.5.5",
+    "vllm>=0.11.0,<=0.18.0",
+    "flashinfer-python==0.6.3",
+    "flashinfer-cubin==0.6.3",
+    "jupyter>=1.1.1",
+    "flash-attn>=2.7.0",
+]
+# Unsloth-backed training (replaces raw TRL + vLLM stack for train_grpo_unsloth.py)
+# Variant is fixed: A100 (Ampere) + CUDA 12.4, inferred from flashinfer==0.6.3 in train extra.
+# Unsloth bundles its own vLLM & transformers builds.
+train-unsloth = [
+    "unsloth[cu124-ampere-torch260]",
+    "trl==0.22.2",
+    "datasets>=4.8.4",
+    "tensorboard>=2.20.0",
+    "tornado>=6.5.5",
+    "jupyter>=1.1.1",
+]
 
 [project.scripts]
 # Server entry point - enables running via: uv run --project . server
@@ -38,3 +63,6 @@ server = "pyre_env.server.app:main"
 include-package-data = true
 packages = ["pyre_env", "pyre_env.server"]
 package-dir = { "pyre_env" = ".", "pyre_env.server" = "server" }
+
+[tool.uv]
+no-build-isolation-package = ["flash-attn"]

run_training_openenv.sh

@@ -25,18 +25,15 @@ echo "Job name  : ${RUN_ID}"
 echo "Port      : ${MASTER_PORT}"
 echo "uv        : ${UV_BIN}"
 
-export CUDA_LAUNCH_BLOCKING=1
-
 bsub -q normal -M 128 -n 1 \
-  -gpu "num=2:gmodel=NVIDIAA100_SXM4_80GB" \
+  -gpu "num=1:gmodel=NVIDIAA100_SXM4_80GB" \
   -o "${OUTPUT_DIR}/output.txt" \
   -e "${OUTPUT_DIR}/error.txt" \
   -J "${RUN_ID}" \
   -env "MASTER_PORT=${MASTER_PORT},VLLM_USE_V1=0" \
-  "${UV_BIN}" run python training/train_grpo_openenv.py \
+  "${UV_BIN}" run python train_grpo_openenv.py \
     --model-id     "${MODEL_NAME}" \
-    --dataset-size 1000 \
+    --dataset-size 200 \
     --output-dir   "${OUTPUT_DIR}" \
     --report-to    "tensorboard" \
-    --seed         42 \
-    --debug
+    --seed         42

run_training_unsloth.sh

@@ -0,0 +1,55 @@
+#!/bin/bash
+# run_training_unsloth.sh — LSF launcher for train_grpo_unsloth.py
+#
+# Usage:
+#   ./run_training_unsloth.sh <model-name>
+#   ./run_training_unsloth.sh unsloth/Qwen3-1.7B
+#   ./run_training_unsloth.sh unsloth/Qwen3-4B --lora-rank 32 --save-merged
+#
+# All arguments after the model name are forwarded verbatim to the Python
+# script, so any train_grpo_unsloth.py flag can be passed here:
+#   ./run_training_unsloth.sh unsloth/Qwen3-4B --dataset-size 500 --save-merged
+set -e
+
+if [ -z "$1" ]; then
+    echo "Usage: $0 <model-name> [extra python args...]"
+    echo "  e.g.: $0 unsloth/Qwen3-1.7B"
+    echo "  e.g.: $0 unsloth/Qwen3-4B --lora-rank 32 --save-merged"
+    exit 1
+fi
+
+MODEL_NAME="$1"
+shift                             # remaining args forwarded to the python script
+EXTRA_ARGS="$*"
+
+MODEL_SAFE=$(echo "$MODEL_NAME" | tr '/:' '--' | tr -cd '[:alnum:]_-')
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+RUN_ID="${TIMESTAMP}_${MODEL_SAFE}"
+
+OUTPUT_DIR="./outputs/${RUN_ID}"
+mkdir -p "${OUTPUT_DIR}"
+
+MASTER_PORT=$(( 29500 + RANDOM % 1000 ))
+UV_BIN=$(which uv)
+
+echo "Model     : ${MODEL_NAME}"
+echo "Output dir: ${OUTPUT_DIR}"
+echo "Job name  : ${RUN_ID}"
+echo "Port      : ${MASTER_PORT}"
+echo "uv        : ${UV_BIN}"
+echo "Extra args: ${EXTRA_ARGS}"
+
+bsub -q normal -M 128 -n 1 \
+  -gpu "num=1:gmodel=NVIDIAA100_SXM4_80GB" \
+  -o "${OUTPUT_DIR}/output.txt" \
+  -e "${OUTPUT_DIR}/error.txt" \
+  -J "${RUN_ID}" \
+  -env "MASTER_PORT=${MASTER_PORT}" \
+  "${UV_BIN}" run python train_grpo_unsloth.py \
+    --model-id     "${MODEL_NAME}" \
+    --dataset-size 200 \
+    --lora-rank    32 \
+    --output-dir   "${OUTPUT_DIR}" \
+    --report-to    "tensorboard" \
+    --seed         42 \
+    ${EXTRA_ARGS}

server/app.py

@@ -10,20 +10,22 @@ Configuration via environment variables:
 
 import os
 from pathlib import Path
-from typing import Any, Dict, Optional
-from pydantic import Field
+from typing import Any, Dict, List, Optional
+from pydantic import Field, BaseModel
 from fastapi import HTTPException
 from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
 from openenv.core.env_server.http_server import create_app
-from pydantic import BaseModel
 from starlette.routing import Route
 
 try:
     from ..models import PyreAction, PyreObservation
     from .pyre_env_environment import PyreEnvironment
+    from .narrative import build_narrative_observation, compute_visible_cells
 except (ImportError, ModuleNotFoundError):
     from models import PyreAction, PyreObservation
     from server.pyre_env_environment import PyreEnvironment
+    from server.narrative import build_narrative_observation, compute_visible_cells
 
 MAX_STEPS = int(os.getenv("PYRE_MAX_STEPS", "150"))
 BASE_SEED = int(os.getenv("PYRE_SEED", "42"))
@@ -83,13 +85,22 @@ STATIC_DIR = Path(__file__).resolve().parent / "static"
 
 @app.get("/")
 def serve_frontend() -> FileResponse:
-    """Serve the bundled RPG viewer from server/static/viewer_rpg.html."""
-    html_path = STATIC_DIR / "viewer_rpg.html"
+    """Serve the React frontend from server/static/index.html."""
+    html_path = STATIC_DIR / "index.html"
     if not html_path.exists():
-        raise HTTPException(status_code=404, detail="Frontend file not found: server/static/viewer_rpg.html")
+        # Fallback to the RPG viewer if index.html is missing
+        rpg_path = STATIC_DIR / "viewer_rpg.html"
+        if rpg_path.exists():
+            return FileResponse(str(rpg_path))
+        raise HTTPException(status_code=404, detail="Frontend file not found.")
     return FileResponse(str(html_path))
 
 
+# Mount the static directory for assets (CSS, JS, etc.)
+if (STATIC_DIR / "assets").exists():
+    app.mount("/assets", StaticFiles(directory=str(STATIC_DIR / "assets")), name="assets")
+
+
 @app.post("/reset")
 def reset_episode(body: ResetRequest = ResetRequest()) -> Dict[str, Any]:
     env = get_stateful_env()
@@ -127,6 +138,133 @@ def get_state() -> Dict[str, Any]:
     return env.state.model_dump()
 
 
+@app.get("/scene")
+def get_scene() -> Dict[str, Any]:
+    """Return a compact scene snapshot for external frontends.
+
+    Response shape
+    --------------
+    labels
+        agent        — position, health, status flags, perception summary
+        episode      — fire parameters, step counters, difficulty
+        map          — grid dimensions, exit positions, door registry
+        surroundings — visible objects, blocked exits, audible signals,
+                       available action hints
+    graph
+        channels     — ordered list of channel names (index guide)
+        channel_info — human-readable description of each channel
+        width / height
+        grid         — grid[y][x] = [cell_type, fire, smoke, is_agent, is_visible]
+                       cell_type: 0=floor 1=wall 2=door_open 3=door_closed
+                                  4=exit 5=obstacle
+                       fire / smoke: 0.0 (none) → 1.0 (max)
+                       is_agent / is_visible: 0 or 1
+    """
+    env = get_stateful_env()
+    st = env.state
+
+    # --- Build structured observation fields (no narrative) ---
+    obs_data = build_narrative_observation(
+        step_count=st.step_count,
+        agent_x=st.agent_x,
+        agent_y=st.agent_y,
+        agent_alive=st.agent_alive,
+        agent_evacuated=st.agent_evacuated,
+        agent_health=st.agent_health,
+        cell_grid=st.cell_grid,
+        fire_grid=st.fire_grid,
+        smoke_grid=st.smoke_grid,
+        exit_positions=st.exit_positions,
+        door_registry=st.door_registry,
+        zone_map=st.zone_map,
+        last_action_feedback=getattr(env, "_last_feedback", ""),
+        wind_dir=st.wind_dir,
+        w=st.grid_w,
+        h=st.grid_h,
+    )
+
+    # --- Visibility set for the graph layer ---
+    if st.agent_alive and not st.agent_evacuated:
+        visible_set = compute_visible_cells(
+            st.agent_x, st.agent_y,
+            st.cell_grid, st.smoke_grid,
+            st.grid_w, st.grid_h,
+        )
+    else:
+        visible_set = set()
+
+    # --- Labels ---
+    labels: Dict[str, Any] = {
+        "agent": {
+            "x": st.agent_x,
+            "y": st.agent_y,
+            "health": st.agent_health,
+            "health_status": obs_data.get("health_status", "Good"),
+            "alive": st.agent_alive,
+            "evacuated": st.agent_evacuated,
+            "location": obs_data.get("location_label", ""),
+            "smoke_level": obs_data.get("smoke_level", "none"),
+            "fire_visible": obs_data.get("fire_visible", False),
+            "fire_direction": obs_data.get("fire_direction", None),
+            "last_action_feedback": obs_data.get("last_action_feedback", ""),
+        },
+        "episode": {
+            "id": st.episode_id,
+            "step": st.step_count,
+            "max_steps": st.max_steps,
+            "template": st.template_name,
+            "difficulty": getattr(env, "_difficulty", "medium"),
+            "wind_dir": st.wind_dir,
+            "fire_spread_rate": st.fire_spread_rate,
+            "humidity": st.humidity,
+            "fire_sources": st.fire_sources_count,
+        },
+        "map": {
+            "width": st.grid_w,
+            "height": st.grid_h,
+            "exit_positions": st.exit_positions,
+            "door_registry": st.door_registry,
+        },
+        "surroundings": {
+            "visible_objects": obs_data.get("visible_objects", []),
+            "blocked_exit_ids": obs_data.get("blocked_exit_ids", []),
+            "audible_signals": obs_data.get("audible_signals", []),
+            "available_actions": obs_data.get("available_actions_hint", []),
+        },
+    }
+
+    # --- 2-D multi-channel grid ---
+    w, h = st.grid_w, st.grid_h
+    grid: List[List[List[float]]] = []
+    for y in range(h):
+        row: List[List[float]] = []
+        for x in range(w):
+            idx = y * w + x
+            cell_type = float(st.cell_grid[idx])
+            fire      = round(st.fire_grid[idx], 4)
+            smoke     = round(st.smoke_grid[idx], 4)
+            is_agent  = 1.0 if (x == st.agent_x and y == st.agent_y) else 0.0
+            is_visible = 1.0 if (x, y) in visible_set else 0.0
+            row.append([cell_type, fire, smoke, is_agent, is_visible])
+        grid.append(row)
+
+    graph: Dict[str, Any] = {
+        "channels": ["cell_type", "fire", "smoke", "is_agent", "is_visible"],
+        "channel_info": {
+            "cell_type": "0=floor 1=wall 2=door_open 3=door_closed 4=exit 5=obstacle",
+            "fire":      "0.0=none to 1.0=fully burning",
+            "smoke":     "0.0=clear to 1.0=dense smoke",
+            "is_agent":  "1 if agent occupies this cell, else 0",
+            "is_visible": "1 if within agent line-of-sight, else 0",
+        },
+        "width":  w,
+        "height": h,
+        "grid":   grid,
+    }
+
+    return {"labels": labels, "graph": graph}
+
+
 def main(host: str = "0.0.0.0", port: int = 8000):
     import uvicorn
     port = int(os.getenv("PORT", port))

server/floor_plan.py

@@ -47,6 +47,7 @@ class FloorPlan:
     spawn_zones: List[Tuple[int, int]]      # valid NPC spawn cells
     agent_spawn_options: List[Tuple[int, int]]
     zone_map: Dict[str, str]                # "{x},{y}" → zone_label
+    static_objects: Dict[str, str] = field(default_factory=dict) # "{x},{y}" → item_type
     fire_min_exit_dist: int = 5             # fire ignition at least this far from any exit
     fuel_map: List[float] = field(default_factory=list)         # fire fuel per cell
     ventilation_map: List[float] = field(default_factory=list)  # smoke decay per cell

server/pyre_env_environment.py

@@ -572,6 +572,7 @@ class PyreEnvironment(Environment):
             is_new_cell=is_new_cell,
             min_exit_dist_reached=self._min_exit_dist_reached,
             rewarded_doors=self._rewarded_doors,
+            reachable_exit_count=len(unblocked_exits(st.exit_positions, st.fire_grid, st.grid_w)),
         )
 
         total = 0.0