Remove private notes, saved HTML data dump, and training zip; expand gitignore

.gitignore

@@ -83,3 +83,9 @@ AGENTS.md
 *External*Meta*OpenEnv*Hackathon*.pdf
 private_notes/
 .private/
+
+# Local scrapes / dumps
+data/
+
+# Training bundle artifacts (regenerate; do not commit)
+sft_*.zip

Summary.txt

@@ -1,283 +0,0 @@
-Wildfire Containment Simulator - Project Summary
-================================================
-
-1. Project Purpose
-This repository implements a grid-based wildfire containment environment designed for reinforcement learning and LLM-agent evaluation. The project follows an OpenEnv-style API with reset(), step(), and state() methods. The main challenge is resource allocation under uncertainty: an agent must deploy crews, use tankers, build firebreaks, and optionally use recon flights to contain a spreading wildfire before it destroys populated cells.
-
-The environment is framed like a hackathon benchmark rather than a full scientific simulator. It is intentionally lightweight, reproducible, and easy to grade, but it still models realistic drivers such as fuel, wind, slope, moisture, smoke, and operational constraints.
-
-2. High-Level Repository Structure
-
-Root files
-- app.py: top-level FastAPI server entry point used by Docker/Hugging Face deployment.
-- inference.py: LLM-driven rollout script that queries an OpenAI-compatible model and runs it on one or more tasks.
-- openenv.yaml: environment metadata, API contract, task definitions, and baseline declarations.
-- pyproject.toml: package metadata, dependencies, and console scripts.
-- README.md: project overview, task descriptions, API examples, and benchmark summary.
-- Dockerfile: container build for serving the environment on port 7860.
-
-Core packages
-- env/: main simulation code.
-- agents/: baseline decision policies.
-- graders/: one grader per difficulty tier.
-- scripts/: evaluation entry point and saved benchmark output.
-- server/: secondary server package entry point for the packaged `serve` / `server` scripts.
-
-Notable extra items
-- There is a nested `Wildfire-Containment-Simulator/README.md` which looks like leftover Hugging Face space metadata rather than active source.
-- There is also a strange root directory named `{env,graders,agents,scripts}` that does not appear to be part of the running code.
-- `venv/`, `__pycache__/`, and `*.egg-info` are local/generated artifacts rather than logical project modules.
-
-3. Main Runtime Architecture
-The central class is env/wildfire_env.py -> WildfireEnv.
-
-WildfireEnv owns five main subsystems:
-- Grid: terrain generation, static cell properties, dynamic cell state, smoke, moisture, and observation filtering.
-- FireSpreadEngine: cellular fire propagation and burn progression.
-- WeatherEngine: wind, humidity, and rain evolution.
-- ResourceManager: crews, tankers, firebreak budget, recon reveal logic, and suppression.
-- RewardCalculator: weighted reward computation and penalties.
-
-The environment is configured by TierConfig objects defined in env/models.py. Three presets exist:
-- easy: 15x15 grid, simple conditions, 1 ignition, no fog/smoke complexity.
-- medium: 25x25 grid, smoke, wind shifts, 2 ignitions, limited recon.
-- hard: 40x40 grid, fog of war, staggered ignition, crew loss event, more resources, longer horizon.
-
-4. Environment Data Model
-env/models.py is the type contract for the whole project. It defines:
-- enums for fuel type, fire state, movement direction, action type, and intensity bins.
-- static and dynamic cell models.
-- weather state and observed weather.
-- crew and tanker state.
-- Action, Observation, and StepResult Pydantic models.
-
-This is important because actions are validated twice:
-- Pydantic validates required fields by action type.
-- WildfireEnv performs semantic checks such as bounds and feasibility.
-
-5. How One Episode Works
-An episode begins with env.reset(task_id, seed).
-
-Reset sequence:
-- picks the tier configuration.
-- seeds NumPy RNG for reproducibility.
-- creates Grid, FireSpreadEngine, WeatherEngine, ResourceManager, and RewardCalculator.
-- resets resource and weather state.
-- ignites initial fire locations chosen to avoid obviously unwinnable starts near population centers.
-- returns the first Observation.
-
-Each env.step(action) follows a fixed sequence:
-- validate the action.
-- execute crew/tanker/firebreak/recon/idle logic.
-- spread the fire.
-- apply crew suppression at deployed crew locations.
-- evolve weather.
-- update terrain moisture.
-- propagate smoke downwind.
-- tick tanker cooldowns.
-- expire recon reveals.
-- trigger hard-mode scripted events such as staggered ignition and forced crew loss.
-- compute reward.
-- check termination.
-- build and return the next observation.
-
-This means action effects and environment dynamics are interleaved every tick. The agent does not directly control fire suppression strength; it controls where crews and resources are positioned, and suppression then occurs automatically through ResourceManager.
-
-6. Terrain and Grid Logic
-env/grid.py generates both static terrain and dynamic cell state.
-
-Static terrain includes:
-- elevation
-- fuel type
-- fuel load
-- water cells
-- population placement
-
-Terrain varies by difficulty:
-- easy is mostly flat grass.
-- medium forms a canyon/valley structure with mixed fuel.
-- hard uses more random mixed terrain, roads, water features, and larger settlements.
-
-Dynamic state includes:
-- current fire state
-- fire intensity
-- moisture
-- suppression level
-- smoke density
-- crew presence
-
-The observation builder applies two visibility systems:
-- smoke occlusion: cells with dense smoke can become UNKNOWN.
-- fog of war: on hard mode, only areas near crews or revealed by recon are visible.
-
-7. Fire Spread Model
-env/fire_spread.py implements an 8-neighbor cellular automaton inspired by Rothermel-style wildfire drivers.
-
-For each burning cell, the engine attempts to ignite neighboring cells using factors based on:
-- target fuel type and fuel load
-- source fire intensity
-- wind alignment
-- uphill/downhill slope
-- moisture
-- suppression level
-- tier-specific spread scaling
-
-After ignition attempts, burning cells progress through intensity growth, plateau, ember state, and burnout. Urban cells are treated specially with higher intensity but lower ignition probability. The model is simplified, but the interaction between terrain, wind, and suppression is coherent enough for benchmarking.
-
-8. Resource Mechanics
-env/resources.py manages the controllable operational layer.
-
-Ground crews
-- start undeployed.
-- can be deployed to safe cells.
-- can move one cell per step in 8 directions.
-- automatically suppress fire on their current cell every tick.
-- can become casualties if intensity spikes too high.
-
-Tankers
-- perform 3x3 retardant drops.
-- increase moisture and suppression while reducing fire intensity.
-- have cooldowns after use.
-- can fail to act when smoke at target is too dense.
-
-Firebreaks
-- are built by adjacent deployed crews.
-- consume a limited budget.
-- convert an unburned cell into a non-flammable FIREBREAK state.
-
-Recon
-- reveals a 10x10 area temporarily.
-- only matters on tiers with fog/hidden information.
-
-9. Reward and Termination
-env/reward.py computes a normalized weighted composite reward with these components:
-- containment
-- population safety
-- efficiency
-- speed
-- area saved
-
-Additional penalties apply for:
-- invalid actions
-- population loss
-- crew casualties
-
-A crew casualty forces reward to 0.0, which makes hard mode especially unforgiving.
-
-Episodes terminate when:
-- the time limit is reached,
-- the fire is fully gone and no delayed ignition remains pending,
-- or all population has been lost.
-
-10. Baseline Agents and Evaluation
-agents/random_agent.py is a lower-bound baseline that samples from available action types.
-
-agents/heuristic_agent.py is the stronger baseline. Its decision stack is:
-- deploy undeployed crews,
-- move endangered crews,
-- protect population,
-- call air support,
-- contain the fire perimeter,
-- use recon when worthwhile,
-- otherwise idle.
-
-scripts/evaluate.py runs both agents across easy, medium, and hard graders with repeated seeds and writes results to scripts/results.json.
-
-The graders are intentionally thin. Each grader:
-- creates a WildfireEnv,
-- resets on a fixed task,
-- repeatedly calls agent.act(obs),
-- returns the final reward.
-
-11. API and Deployment Layer
-There are two HTTP server entry points:
-- app.py at repo root
-- server/app.py inside the package
-
-Both expose similar FastAPI endpoints:
-- GET /
-- GET /health
-- POST /reset
-- POST /step
-- GET /state
-
-Docker currently launches `python app.py`, while pyproject console scripts point to `server.app:main`. Functionally they are close, but this duplication is worth cleaning up later because it creates two sources of truth for the same service.
-
-12. Inference Flow
-inference.py is the LLM evaluation script. It:
-- reads API credentials and model settings from environment variables,
-- converts the current observation into a compact prompt,
-- asks an OpenAI-compatible chat model for exactly one JSON action,
-- parses the action into the typed Action model,
-- steps the environment until completion,
-- prints structured logs for automated scoring.
-
-This is the bridge between the environment and external language models.
-
-13. Current Project State
-The project is structurally complete for a hackathon submission:
-- environment logic exists and is modular.
-- typed schemas are in place.
-- baseline agents exist.
-- graders and evaluation scripts exist.
-- REST serving and Docker deployment exist.
-
-The codebase is not organized like a production RL training project yet. There is no dedicated training loop, no test suite beyond graders, and some duplication/artifact folders remain in the repo. Still, the simulation itself is reasonably well separated and understandable, especially inside env/.
-
-15. Changes Log
-===============
-
-Before (original state):
-- app.py and server/app.py were duplicate full server implementations with no single source of truth.
-- Dockerfile pointed to `python app.py` (the duplicate).
-- __pycache__/ directories (16 files) were committed to git alongside a nested Wildfire-Containment-Simulator/ submodule and an empty {env,graders,agents,scripts}/ artifact directory.
-- .gitignore only excluded `venv`, nothing else.
-- No test suite existed.
-- requirements.txt had no test dependencies.
-- StepResult.reward was constrained to [0.0, 1.0].
-- Reward was a single normalized composite in [0, 1] with no terminal spike structure.
-
-After (Prompt 1 — Repo Cleanup & Test Scaffolding):
-- app.py reduced to a one-line shim: `from server.app import main; main()`.
-- Dockerfile CMD updated to `python server/app.py`.
-- All committed __pycache__ files removed from git index; nested submodule and brace artifact dir deleted.
-- .gitignore expanded to cover __pycache__/, *.egg-info/, venv/, .venv/, *.pyc, .pytest_cache/, .ruff_cache/, checkpoints/, results/.
-- tests/ directory created with conftest.py (fresh_env fixture) and test_smoke.py (3 passing tests: tier resets, idle stability, determinism).
-- pytest and pytest-cov added to requirements.txt.
-
-After (Prompt 2 — Reward Restructuring):
-- StepResult.reward constraint removed; rewards can now range freely (e.g. -5 to +8).
-- RewardCalculator gained two new methods:
-  - compute_step_reward(): dense delta-based reward each step (delta_containment×0.4 + delta_pop_safety×0.4 − 0.1 if redundant).
-  - compute_terminal_reward(): sparse terminal bonus (+5 if all pop saved + efficiency bonus, or −3×loss_pct; −2 stacked for crew casualty).
-- wildfire_env.py tracks _prev_action, _invalid_action_count, _crew_casualty_occurred across episodes; step() now returns step_reward + terminal_reward.
-- Legacy composite reward preserved in info["legacy_reward"] for backward compatibility.
-- tests/test_reward.py added with 4 passing tests.
-- Heuristic agent on easy tier now scores ~6.66 ± 1.64 (target +5 to +8 range confirmed).
-
-After (Prompt 3 — Observation-to-Text Serializer):
-- Created env/serialization.py with serialize_observation() producing structured LLM prompts.
-- Sections: SITUATION, GRID SUMMARY (with BFS bounding-box clustering), RESOURCES, RECENT EVENTS, Available actions.
-- BFS clustering caps at 5 regions per category; fog-of-war cells marked [?].
-- tests/test_serialization.py added with 3 passing tests.
-
-After (Prompt 4 — LLM Action Parser):
-- Created env/action_parser.py with 3-layer parse_action() fallback: JSON → regex → safe_idle.
-- _extract_json_block() strips ```json fences and surrounding text.
-- Out-of-bounds coords and hallucinated action types downgrade to IDLE, never crash.
-- tests/test_action_parser.py added with 8 passing tests.
-
-After (Prompt 5 — Replay / GIF Renderer):
-- Created env/rendering.py with render_frame() (matplotlib 800×800px, fire colors, crew markers, stats strip + wind arrow) and render_episode_gif() (imageio 5 fps).
-- Created scripts/replay.py CLI (--tier, --seed, --agent, --output); saves GIF + final PNG.
-- requirements.txt updated: matplotlib>=3.7, imageio>=2.28 added.
-- tests/test_rendering.py added with 2 passing tests (frame shape, GIF >10KB).
-
-14. Practical Takeaway
-If you want to extend this repository, the main places to work are:
-- env/: for simulation rules and observation design.
-- agents/: for better baseline or learned policies.
-- scripts/evaluate.py and inference.py: for benchmarking and external-agent experiments.
-- server/app.py or app.py: for deployment cleanup and API consistency.
-
-The project is best understood as a benchmark environment plus baseline agents, not as a full end-to-end RL training system.

colab_prompts.md

@@ -1,607 +0,0 @@
-# Colab training prompts (feed to Claude, in order)
-
-Each prompt is self-contained — paste as a fresh message with no prior context.
-
----
-
-## Prompt 1 — SFT data generator script
-
-```
-Write a standalone Python script `scripts/generate_sft_data.py` for the Wildfire Containment Simulator project.
-
-PURPOSE: Generate supervised fine-tuning (SFT) training examples by running the HeuristicAgent through episodes and recording (prompt, action) pairs at every step.
-
-REPO STRUCTURE (files that exist):
-- env/wildfire_env.py — WildfireEnv with reset(task_id, seed) and step(action)
-- env/serialization.py — serialize_observation(obs, step_num, max_steps, tier="", prev_cells_burning=0) -> str
-- agents/heuristic_agent.py — HeuristicAgent with act(obs) -> Action
-- env/models.py — TIER_EASY(episode_length=80), TIER_MEDIUM(episode_length=150), TIER_HARD(episode_length=300)
-- env/action_parser.py — parse_action(text, obs) -> (Action, status)
-
-SYSTEM_PROMPT constant to use in every example:
-"You are an AI Incident Commander managing wildfire containment. You will receive a situation briefing each step. Respond with ONLY a valid JSON action object and nothing else. Example: {\"action_type\": \"idle\"}"
-
-REQUIREMENTS:
-1. For each tier ("easy", "medium", "hard"), for each seed in a configurable range:
-   a. Reset the env
-   b. Run the heuristic for a random offset (0 to min(30, max_steps//4)) steps to get mid-episode states
-   c. Run the heuristic to EPISODE COMPLETION (env.done == True), recording every step
-   d. After the episode is complete, check env.state()["population_lost"] == 0. Only keep examples
-      from successful episodes (pop_lost == 0 at end). Discard the whole episode otherwise.
-   e. From the kept episodes, record every step as a training example EXCEPT: filter out IDLE
-      actions unless they represent more than 30% of the episode's actions (keep a realistic idle rate).
-      Concretely: keep all non-IDLE steps, then randomly sample IDLE steps to reach at most 20% of
-      total examples per episode.
-   f. Each example: {"messages": [{"role": "system", ...}, {"role": "user", "content": prompt_text}],
-      "completion": action_json_string, "tier": tier, "seed": seed, "step": step_num}
-   g. The "completion" field is the action serialised as compact JSON (action.model_dump_json(exclude_none=True))
-
-2. Track prev_cells_burning across steps to pass to serialize_observation for spread delta.
-
-3. Target counts after filtering: easy=2000 examples, medium=1500, hard=800.
-   Iterate seeds starting from 0, incrementing by 1, until targets are met.
-
-4. Save to training/sft_data.jsonl (one JSON object per line). Print progress every 50 seeds.
-   Print final tier distribution before exiting.
-
-5. Add argparse: --output (default training/sft_data.jsonl), --easy-seeds N (max seeds to try),
-   --medium-seeds N, --hard-seeds N
-
-IMPORTANT:
-- The script runs locally, not in Colab. Use sys.path.insert(0, project_root) to make env/ importable.
-- No GPU needed.
-- Do NOT filter mid-episode observations — they are intentionally included for training diversity.
-  The per-episode success filter (pop_lost==0) applies to the whole episode, not individual steps.
-```
-
----
-
-## Prompt 2 — SFT training notebook
-
-```
-Write a complete Google Colab notebook `training/sft_colab.ipynb` for supervised fine-tuning of
-Qwen2.5-7B-Instruct on wildfire incident command data.
-
-CONTEXT:
-- Input: training/sft_data.jsonl, where each line has:
-  {"messages": [{"role":"system","content":"..."}, {"role":"user","content":"..."}],
-   "completion": "{\"action_type\":...}", "tier": "easy", "seed": 42, "step": 5}
-- Goal: teach the model to output valid JSON action objects given wildfire observations
-- Hardware target: A100 40GB on Colab (HF credits)
-
-NOTEBOOK SECTIONS:
-
-Section 1 — Install
-- pip install: unsloth[colab-new] from git, trl==0.15.2, datasets==3.4.1
-- assert torch.cuda.is_available(), print GPU name and total memory
-
-Section 2 — Load Model
-- unsloth FastLanguageModel.from_pretrained("unsloth/Qwen2.5-7B-Instruct",
-    max_seq_length=2048, load_in_4bit=True)
-- FastLanguageModel.get_peft_model with r=32, lora_alpha=64, lora_dropout=0.05
-- target_modules=['q_proj','k_proj','v_proj','o_proj','gate_proj','up_proj','down_proj']
-- Use pad_token = eos_token if no pad token exists
-
-Section 3 — Load Data
-- Read sft_data.jsonl
-- Format each example: apply tokenizer.apply_chat_template to the messages list, then append the
-  completion string as the assistant turn. The final string is the full conversation for causal LM loss.
-- Use datasets.Dataset.from_list
-- Print tier distribution (counts per tier)
-- Train/val split: 95/5
-
-Section 4 — Train
-- Use trl SFTTrainer with:
-  - per_device_train_batch_size=2, gradient_accumulation_steps=4 (effective batch 8)
-  - num_train_epochs=1
-  - learning_rate=2e-4, warmup_ratio=0.05, lr_scheduler_type="cosine"
-  - logging_steps=10, save_steps=100, save_total_limit=2
-  - output_dir="./sft_checkpoints"
-  - report_to="none"
-  - max_seq_length=2048, packing=True
-
-Section 5 — Quick Eval (runs in Colab, requires env imports)
-- Add sys.path and import WildfireEnv, serialize_observation, parse_action
-- Run 10 full episodes (seeds 42–51) on easy tier with the trained model driving EVERY step:
-  - FastLanguageModel.for_inference(model)
-  - For each step: build messages, apply_chat_template, model.generate(max_new_tokens=128),
-    decode, parse_action(completion, obs), env.step(action)
-  - Accumulate total_reward; track parse_status counts
-- Print: mean reward, std, json_success_rate, mean pop_saved_pct
-- assert mean_reward > 2.0, "SFT warm-up insufficient — do not proceed to GRPO"
-- FastLanguageModel.for_training(model) before returning
-
-Section 6 — Save
-- model.save_pretrained("./sft_final")
-- tokenizer.save_pretrained("./sft_final")
-- model.push_to_hub("YOUR_HF_USERNAME/wildfire-sft-7b")  # leave as placeholder
-- !zip -r sft_final.zip ./sft_final
-- from google.colab import files; files.download("sft_final.zip")
-
-IMPORTANT NOTES:
-- parse_action(text, obs) requires a real obs object (it reads obs.grid). Always pass the current obs.
-- serialize_observation signature: (obs, step_num, max_steps, tier="", prev_cells_burning=0)
-- Instantiate a fresh HeuristicAgent (if used) for each episode — it has step_count state.
-```
-
----
-
-## Prompt 3 — GRPO training notebook
-
-```
-Write a complete Google Colab notebook `training/grpo_v2_colab.ipynb` for GRPO reinforcement
-learning of a wildfire incident command model. This is a redesigned version that fixes five
-critical issues from the previous attempt.
-
-FIVE ISSUES FIXED IN THIS VERSION (do not reintroduce them):
-
-Issue 1 — Prompt/reward state mismatch (critical):
-  Previous: dataset used mid-episode prompts; reward_fn picked a random seed → model was scored
-  in a completely different env state than the one that produced its prompt.
-  Fix: Dataset uses step-0 prompts ONLY. Each row stores the seed used. The reward_fn resets the
-  env to that exact (tier, seed) pair before scoring the completion. Prompt state = reward state.
-
-Issue 2 — Truncated rollout reward incomparable to curriculum thresholds (critical):
-  Previous: 15-step rollouts never reached min_active_steps=25, so terminal reward (+5.0) never
-  fired. GRPO rewards capped at ~1-2 while thresholds were set to 7.0/5.5. Promotion never happened.
-  Fix: The reward function runs the FULL episode to completion (model's 1 action at step 0, then
-  heuristic until env.done). Terminal reward is always included. Reward is comparable to baselines.
-
-Issue 3 — Wasted inner model generations:
-  Previous: reward_fn called model.generate() 7 extra times per completion inside the reward loop.
-  GRPO gradients only flow through the originally sampled completion, making inner model steps
-  expensive noise with no gradient benefit.
-  Fix: MODEL_STEPS = 1. Only the sampled completion is applied. Heuristic drives everything after.
-
-Issue 4 — GRPO loop too slow:
-  Consequence of Issue 3. Fix is same: MODEL_STEPS = 1 reduces reward_fn generate calls to 0.
-
-Issue 5 — parse_action(text, None) crashes:
-  The parser reads obs.grid at line 1. Cannot pass None.
-  Fix: Use a standalone check_json_format(text) function in the format reward that does its own
-  JSON validation without needing an obs.
-
-CORRECT FULL-EPISODE BASELINES (from scripts/results.json):
-  random:    easy=+6.23  medium=+1.31  hard=+2.16
-  heuristic: easy=+7.53  medium=+6.31  hard=+4.74
-
-STARTING POINT: SFT checkpoint at "YOUR_HF_USERNAME/wildfire-sft-7b" (or local sft_final.zip)
-
-EXISTING ENV FILES (correct and working — do not reimplement):
-- env/wildfire_env.py: WildfireEnv, reset(task_id, seed), step(action)->StepResult(observation,reward,done,info)
-- env/serialization.py: serialize_observation(obs, step_num, max_steps, tier="", prev_cells_burning=0)->str
-- env/action_parser.py: parse_action(text, obs)->(Action, status); status in ["json_success","regex_fallback","safe_idle"]
-- agents/heuristic_agent.py: HeuristicAgent().act(obs)->Action  [stateful: re-instantiate per episode]
-- env/curriculum.py: CurriculumController(start_tier, thresholds); after_episode(reward)->Optional[str]; get_tier()->str
-- env/models.py: TIER_EASY(episode_length=80), TIER_MEDIUM(episode_length=150), TIER_HARD(episode_length=300)
-
-NOTEBOOK SECTIONS:
-
-Section 1 — Install and assert GPU
-- pip install: unsloth[colab-new] from git, trl==0.15.2, datasets==3.4.1, wandb
-- assert torch.cuda.is_available()
-- print GPU name and total VRAM
-
-Section 2 — Load SFT checkpoint
-- FastLanguageModel.from_pretrained("YOUR_HF_USERNAME/wildfire-sft-7b", load_in_4bit=True, max_seq_length=2048)
-  OR if loading from local zip: load base model first, then model.load_adapter(sft_path, adapter_name="default")
-- Same LoRA: r=32, lora_alpha=64, target_modules=['q_proj','k_proj','v_proj','o_proj','gate_proj','up_proj','down_proj']
-
-Section 3 — Constants and controller setup
-
-```python
-import os, random, json
-import torch
-from env import WildfireEnv
-from env.serialization import serialize_observation
-from env.action_parser import parse_action
-from agents.heuristic_agent import HeuristicAgent
-from env.curriculum import CurriculumController
-from datasets import Dataset
-
-SEED_POOL = list(range(100))       # training seeds; eval uses 200+
-TIER_MAX_STEPS = {'easy': 80, 'medium': 150, 'hard': 300}
-SYSTEM_PROMPT = (
-    'You are an AI Incident Commander managing wildfire containment. '
-    'You will receive a situation briefing each step. '
-    'Respond with ONLY a valid JSON action object and nothing else. '
-    'Example: {"action_type": "idle"}'
-)
-
-# Thresholds calibrated to full-episode reward with heuristic continuation.
-# Promote easy→medium once model's first action consistently beats random (+6.23).
-# Promote medium→hard once model demonstrates meaningful improvement over random (+1.31).
-controller = CurriculumController(
-    start_tier='easy',
-    thresholds={'easy': 6.5, 'medium': 3.5},
-)
-
-os.makedirs('training/samples', exist_ok=True)
-_reward_call_count = 0
-```
-
-Section 4 — Standalone JSON format checker (replaces parse_action for format reward)
-
-```python
-import json as _json
-from env.models import ActionType as _AT
-
-_VALID_ACTION_TYPES = {a.value for a in _AT}
-
-def check_json_format(text: str) -> str:
-    """
-    Validate LLM output format without needing an obs object.
-    Returns "json_success", "regex_fallback", or "safe_idle".
-    Does NOT use parse_action — avoids the obs.grid dependency.
-    """
-    # Strip code fences
-    import re
-    text = re.sub(r"```(?:json)?\s*", "", text).replace("```", "")
-    start = text.find("{")
-    if start == -1:
-        return "safe_idle"
-    depth = 0
-    end = -1
-    for i, ch in enumerate(text[start:], start=start):
-        if ch == "{": depth += 1
-        elif ch == "}":
-            depth -= 1
-            if depth == 0:
-                end = i
-                break
-    if end == -1:
-        return "safe_idle"
-    try:
-        obj = _json.loads(text[start:end+1])
-        if not isinstance(obj, dict):
-            return "safe_idle"
-        at = str(obj.get("action_type", "")).lower()
-        if at in _VALID_ACTION_TYPES:
-            return "json_success"
-        return "regex_fallback"   # valid JSON but unrecognised action_type
-    except Exception:
-        return "regex_fallback"   # JSON parse failed but had braces
-```
-
-Section 5 — Two reward functions
-
-reward_fn_outcome(completions, prompts, tier=None, seed=None, **kwargs):
-  """
-  Score each GRPO completion by:
-    1. Resetting the env to the EXACT (tier, seed) that generated the prompt (Issue 1 fix).
-    2. Applying the sampled completion as the single first action (MODEL_STEPS=1, Issue 3/4 fix).
-    3. Running HeuristicAgent until episode completion (Issue 2 fix — captures terminal reward).
-  
-  tier and seed are dataset columns forwarded by GRPOTrainer.
-  """
-  global _reward_call_count
-  _reward_call_count += 1
-  rewards = []
-
-  for i, completion in enumerate(completions):
-      ep_tier = tier[i] if tier is not None else controller.get_tier()
-      ep_seed = seed[i] if seed is not None else random.choice(SEED_POOL)
-
-      env = WildfireEnv()
-      obs = env.reset(task_id=ep_tier, seed=ep_seed)   # step-0: matches prompt state exactly
-      total_reward = 0.0
-
-      # Apply the sampled completion as step 0
-      text = completion if isinstance(completion, str) else completion[0]['content']
-      action, _ = parse_action(text, obs)
-      result = env.step(action)
-      total_reward += result.reward
-      obs = result.observation
-
-      # Heuristic drives everything after (full episode to capture terminal reward)
-      heuristic = HeuristicAgent()   # fresh instance per episode (stateful step_count)
-      while not env.done:
-          action = heuristic.act(obs)
-          result = env.step(action)
-          total_reward += result.reward
-          obs = result.observation
-
-      rewards.append(total_reward)
-
-  # Update curriculum (once per batch, not per completion)
-  mean_r = sum(rewards) / len(rewards)
-  promoted = controller.after_episode(mean_r)
-  if promoted:
-      print(f"  *** Curriculum promoted to: {promoted} (mean batch reward={mean_r:.2f}) ***")
-
-  # Sample completions to disk for inspection (Issue 4 in HACKATHON_ALIGNMENT.md)
-  if _reward_call_count % 10 == 0:
-      sample_path = f'training/samples/call_{_reward_call_count}.txt'
-      with open(sample_path, 'w') as f:
-          f.write(f"call={_reward_call_count}  tier={tier[0] if tier else '?'}  reward={rewards[0]:.3f}\n")
-          f.write("---\n")
-          c = completions[0]
-          f.write(c if isinstance(c, str) else c[0]['content'])
-          f.write("\n")
-
-  return rewards
-
-
-reward_fn_format(completions, prompts, **kwargs):
-  """
-  Scores JSON formatting quality using check_json_format() (no obs needed).
-  Runs independently of the env — fast and always well-defined.
-  """
-  rewards = []
-  for completion in completions:
-      text = completion if isinstance(completion, str) else completion[0]['content']
-      status = check_json_format(text)
-      if status == "json_success":     r = 0.15
-      elif status == "regex_fallback": r = 0.0
-      else:                            r = -0.20   # safe_idle / garbage
-      rewards.append(r)
-  return rewards
-
-Section 6 — Dataset builder (step-0 only; stores seed for reward alignment)
-
-```python
-def build_prompt_dataset(n=200):
-    """
-    Build step-0 prompts for the current curriculum tier.
-    Stores the seed in each row so reward_fn can replay the exact same env state.
-    No mid-episode offset — GRPO prompt and reward state are always step-0.
-    Mid-episode diversity is handled by SFT, not GRPO.
-    """
-    rows = []
-    env_tmp = WildfireEnv()
-    tier = controller.get_tier()
-    max_steps = TIER_MAX_STEPS[tier]
-
-    for i in range(n):
-        seed = SEED_POOL[i % len(SEED_POOL)]
-        obs = env_tmp.reset(task_id=tier, seed=seed)   # step-0
-        prompt = serialize_observation(obs, 0, max_steps, tier=tier, prev_cells_burning=0)
-        rows.append({
-            'prompt': [
-                {'role': 'system', 'content': SYSTEM_PROMPT},
-                {'role': 'user',   'content': prompt},
-            ],
-            'tier': tier,
-            'seed': seed,   # forwarded to reward_fn_outcome for exact state replay
-        })
-    return rows
-```
-
-Section 7 — CurriculumDatasetCallback
-
-Implement a trl TrainerCallback subclass that rebuilds the training dataset whenever the
-curriculum controller promotes to a new tier:
-
-```python
-from trl import TrainerCallback
-
-class CurriculumDatasetCallback(TrainerCallback):
-    def __init__(self, trainer_ref):
-        self._trainer = trainer_ref
-        self._last_tier = controller.get_tier()
-
-    def on_step_end(self, args, state, control, **kwargs):
-        current_tier = controller.get_tier()
-        if current_tier != self._last_tier:
-            print(f"  Rebuilding dataset for tier: {current_tier}")
-            new_ds = Dataset.from_list(build_prompt_dataset(200))
-            self._trainer.train_dataset = new_ds
-            self._last_tier = current_tier
-```
-
-Section 8 — GRPOTrainer setup
-
-```python
-from trl import GRPOTrainer, GRPOConfig
-
-grpo_config = GRPOConfig(
-    output_dir="./grpo_checkpoints",
-    num_generations=8,
-    learning_rate=3e-6,
-    max_steps=400,
-    save_steps=20,
-    per_device_train_batch_size=1,
-    gradient_accumulation_steps=4,
-    max_completion_length=192,   # enough for any valid action JSON
-    logging_steps=1,
-    report_to="wandb",
-)
-
-FastLanguageModel.for_training(model)
-
-dataset = Dataset.from_list(build_prompt_dataset(200))
-
-trainer = GRPOTrainer(
-    model=model,
-    processing_class=tokenizer,
-    reward_funcs=[reward_fn_outcome, reward_fn_format],
-    args=grpo_config,
-    train_dataset=dataset,
-)
-trainer.add_callback(CurriculumDatasetCallback(trainer))
-```
-
-Section 9 — Run training
-
-```python
-import wandb
-wandb.init(project="wildfire-grpo", name="qwen7b-v2")
-
-print(f"Starting GRPO — {grpo_config.max_steps} steps, {grpo_config.num_generations} gen/prompt")
-print(f"Reward: 1 model step at step-0, heuristic continuation to episode completion")
-print(f"Start tier: {controller.get_tier()}")
-
-trainer.train()
-print("Training complete.")
-
-history = controller.get_history()
-stats = [{'step': ep, 'tier': t, 'mean_reward': r} for ep, t, r in history]
-with open('./training_stats.json', 'w') as f:
-    json.dump(stats, f, indent=2)
-print("Stats saved -> training_stats.json")
-```
-
-Section 10 — Evaluate vs baselines
-
-- Load scripts/results.json for heuristic and random baseline scores
-- For each tier in [easy, medium, hard], run 15 full episodes (seeds 42–56):
-  - FastLanguageModel.for_inference(model)
-  - Instantiate a FRESH LLMAgent per episode (it is stateful: _step, _prev_burning, parse counters)
-  - Model drives every step until env.done
-  - Record total_reward, pop_saved_pct, json_success_rate
-- Print comparison table: Trained vs Heuristic vs Random, including vs_heuristic delta
-- Print JSON success rate per tier
-- assert: for at least 1 tier, trained_mean > heuristic_mean - 1.0
-
-LLMAgent class to implement:
-```python
-class LLMAgent:
-    def __init__(self, model, tokenizer, tier, max_steps):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.tier = tier
-        self.max_steps = max_steps
-        self._step = 0
-        self._prev_burning = 0
-        self.json_success = self.regex_fallback = self.safe_idle = 0
-
-    def act(self, obs):
-        prompt = serialize_observation(obs, self._step, self.max_steps,
-                                       tier=self.tier,
-                                       prev_cells_burning=self._prev_burning)
-        self._prev_burning = obs.stats.cells_burning
-        messages = [{"role": "system", "content": SYSTEM_PROMPT},
-                    {"role": "user",   "content": prompt}]
-        input_ids = tokenizer.apply_chat_template(
-            messages, tokenize=True, add_generation_prompt=True, return_tensors='pt'
-        ).to(model.device)
-        with torch.no_grad():
-            out = model.generate(input_ids, max_new_tokens=128,
-                                 pad_token_id=tokenizer.eos_token_id)
-        text = tokenizer.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True)
-        action, status = parse_action(text, obs)
-        if status == "json_success":      self.json_success += 1
-        elif status == "regex_fallback":  self.regex_fallback += 1
-        else:                             self.safe_idle += 1
-        self._step += 1
-        return action
-```
-
-Section 11 — Save and push
-
-- model.save_pretrained("./grpo_final")
-- tokenizer.save_pretrained("./grpo_final")
-- model.push_to_hub("YOUR_HF_USERNAME/wildfire-grpo-7b")
-- !zip -r grpo_final.zip ./grpo_final
-- files.download("grpo_final.zip")
-
-IMPLEMENTATION CHECKLIST:
-[ ] reward_fn_outcome uses seed from dataset row, NOT random.choice(SEED_POOL)
-[ ] reward_fn_outcome resets env with env.reset(task_id=ep_tier, seed=ep_seed) — step-0 only
-[ ] reward_fn_outcome runs heuristic until env.done (not a fixed step count)
-[ ] reward_fn_format calls check_json_format(), NOT parse_action(text, None)
-[ ] build_prompt_dataset has no step offset — always step-0 — and always saves seed in the row
-[ ] CurriculumDatasetCallback triggers dataset rebuild on tier change
-[ ] LLMAgent instantiated FRESH per episode in the eval section
-[ ] FastLanguageModel.for_inference/for_training toggled correctly around eval calls
-[ ] WildfireEnv instantiated fresh per completion in reward_fn_outcome (not shared)
-[ ] HeuristicAgent instantiated fresh per episode in reward_fn_outcome (it has step_count state)
-```
-
----
-
-## Prompt 4 — Evaluation and comparison script
-
-```
-Write a standalone Python script `scripts/eval_trained_model.py` that evaluates a trained HF
-adapter model against the heuristic and random baselines on the Wildfire Containment Simulator.
-
-PURPOSE: Source-of-truth comparison table after training is complete.
-Saves results to scripts/trained_results.json.
-
-INPUTS (argparse):
-- --model-path: HF hub ID or local path to the trained adapter (e.g. "username/wildfire-grpo-7b")
-- --base-model: base model (default "unsloth/Qwen2.5-7B-Instruct")
-- --num-seeds: evaluation seeds per tier (default 15, uses seeds 200–214 to avoid train overlap)
-- --tiers: space-separated list (default "easy medium hard")
-
-EXISTING FILES:
-- graders/grader_easy.py, grader_medium.py, grader_hard.py — grade(agent, seed) -> (float, details_dict)
-- agents/heuristic_agent.py — HeuristicAgent
-- agents/random_agent.py — RandomAgent(seed=N)
-- scripts/results.json — existing baselines
-- env/wildfire_env.py, env/serialization.py, env/action_parser.py
-
-SYSTEM_PROMPT = (
-    'You are an AI Incident Commander managing wildfire containment. '
-    'You will receive a situation briefing each step. '
-    'Respond with ONLY a valid JSON action object and nothing else. '
-    'Example: {"action_type": "idle"}'
-)
-
-LLM AGENT CLASS (stateful — MUST be instantiated fresh per episode):
-```python
-class LLMAgent:
-    """
-    Wraps the trained model for grader compatibility.
-    Must be re-instantiated for every episode — _step and _prev_burning
-    are per-episode state and will produce wrong prompts if reused.
-    """
-    def __init__(self, model, tokenizer, tier, max_steps):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.tier = tier
-        self.max_steps = max_steps
-        self._step = 0
-        self._prev_burning = 0
-        self.json_success = self.regex_fallback = self.safe_idle = 0
-
-    def act(self, obs):
-        import torch
-        prompt = serialize_observation(obs, self._step, self.max_steps,
-                                       tier=self.tier,
-                                       prev_cells_burning=self._prev_burning)
-        self._prev_burning = obs.stats.cells_burning
-        messages = [{"role": "system", "content": SYSTEM_PROMPT},
-                    {"role": "user",   "content": prompt}]
-        input_ids = self.tokenizer.apply_chat_template(
-            messages, tokenize=True, add_generation_prompt=True, return_tensors='pt'
-        ).to(self.model.device)
-        with torch.no_grad():
-            out = self.model.generate(input_ids, max_new_tokens=128,
-                                      pad_token_id=self.tokenizer.eos_token_id)
-        text = self.tokenizer.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True)
-        action, status = parse_action(text, obs)
-        if status == "json_success":      self.json_success += 1
-        elif status == "regex_fallback":  self.regex_fallback += 1
-        else:                             self.safe_idle += 1
-        self._step += 1
-        return action
-```
-
-GRADER WRAPPER (because graders pass agent to grade(), so agent is shared across seeds by default):
-For LLMAgent, override this by not using grade() directly. Instead inline the grader logic and
-instantiate a fresh LLMAgent(model, tokenizer, tier, max_steps) before EACH episode.
-
-OUTPUT FORMAT:
-```
-=== Evaluation: Trained Model vs Baselines ===
-Model:  username/wildfire-grpo-7b
-Seeds:  200-214  (15 per tier)
-
-Tier      Trained    Heuristic   Random     vs Heuristic
--------------------------------------------------------
-easy      +7.21±0.3  +7.53±0.1  +6.23±3.1  -0.32
-medium    +6.89±1.2  +6.31±2.8  +1.31±3.2  +0.58 ✓
-hard      +4.12±2.1  +4.74±3.8  +2.16±3.0  -0.62
-
-JSON success rate:  easy=91.2%  medium=88.4%  hard=85.1%
-Pop saved rate:     easy=100%   medium=97%    hard=93%
-```
-
-Also save to scripts/trained_results.json in the same format as scripts/results.json, with an
-additional "json_success_rate" field per tier.
-```
-
----
-

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-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZMkido18Smxg.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZMkidg18Smxg.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZMkidv18Smxg.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZMkidj18Smxg.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZMkidi18Smxg.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZMkids18Q.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7qsDJT9g.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7jsDJT9g.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7rsDJT9g.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7ksDJT9g.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7osDJT9g.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7psDJT9g.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDI.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZY4lCdh18Smxg.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZY4lCdo18Smxg.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZY4lCdg18Smxg.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZY4lCdv18Smxg.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZY4lCdj18Smxg.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZY4lCdi18Smxg.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZY4lCds18Q.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZclSdh18Smxg.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZclSdo18Smxg.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZclSdg18Smxg.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZclSdv18Smxg.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZclSdj18Smxg.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZclSdi18Smxg.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: italic;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKwdSBYKcSV-LCoeQqfX1RYOo3qPZZclSds18Q.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 200;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i94_wmhduz8A.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 200;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i94_wkxduz8A.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 200;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i94_wmxduz8A.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 200;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i94_wlBduz8A.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 200;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i94_wmBduz8A.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 200;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i94_wmRduz8A.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 200;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i94_wlxdu.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmhduz8A.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwkxduz8A.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmxduz8A.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlBduz8A.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmBduz8A.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmRduz8A.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 300;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdu.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNa7lqDY.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK3dSBYKcSV-LCoeQqfX1RYOo3qPK7lqDY.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNK7lqDY.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK3dSBYKcSV-LCoeQqfX1RYOo3qO67lqDY.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK3dSBYKcSV-LCoeQqfX1RYOo3qN67lqDY.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNq7lqDY.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7l.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmhduz8A.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwkxduz8A.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmxduz8A.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlBduz8A.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmBduz8A.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmRduz8A.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlxdu.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmhduz8A.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwkxduz8A.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* greek-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmxduz8A.woff2) format('woff2');
-  unicode-range: U+1F00-1FFF;
-}
-/* greek */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlBduz8A.woff2) format('woff2');
-  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmBduz8A.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmRduz8A.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'Source Sans Pro';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/sourcesanspro/v23/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdu.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}

data/Wildfire Grpo Training - a Hugging Face Space by Eshit_files/css2(1)

@@ -1,135 +0,0 @@
-/* cyrillic-ext */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F63fjptAgt5VM-kVkqdyU8n1iIq129k.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F63fjptAgt5VM-kVkqdyU8n1isq129k.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F63fjptAgt5VM-kVkqdyU8n1iAq129k.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F63fjptAgt5VM-kVkqdyU8n1iEq129k.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 400;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F63fjptAgt5VM-kVkqdyU8n1i8q1w.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3vAOwl1FgtIU.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3vAOwlRFgtIU.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3vAOwl9FgtIU.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3vAOwl5FgtIU.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 600;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3vAOwlBFgg.woff2) format('woff2');
-  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
-}
-/* cyrillic-ext */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3pQPwl1FgtIU.woff2) format('woff2');
-  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
-}
-/* cyrillic */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3pQPwlRFgtIU.woff2) format('woff2');
-  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
-}
-/* vietnamese */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3pQPwl9FgtIU.woff2) format('woff2');
-  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
-}
-/* latin-ext */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
-  src: url(https://fonts.gstatic.com/s/ibmplexmono/v20/-F6qfjptAgt5VM-kVkqdyU8n3pQPwl5FgtIU.woff2) format('woff2');
-  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
-}
-/* latin */
-@font-face {
-  font-family: 'IBM Plex Mono';
-  font-style: normal;
-  font-weight: 700;
-  font-display: swap;
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implementation_plan.md

@@ -1,35 +0,0 @@
-# Addressing the Heuristic Performance for the Hackathon Pitch
-
-The fact that the heuristic agent performs so well is a common challenge in RL hackathons. If the baseline is unbeatable, the RL training seems pointless. 
-
-To solve this, we will use **The "Commander's Intent" Narrative**. We won't just "break" the heuristic; we will expose its fundamental weakness: **it is a rigid expert system that cannot read natural language or follow instructions.** 
-
-This directly aligns with the hackathon's **Theme 2: Long-Horizon Planning & Instruction Following**.
-
-## The Narrative for the Judges
-"Our heuristic baseline is an expert system that aggressively fights fires to save lives. But like all rigid heuristics, it suffers from 'tunnel vision'. It cannot read natural language briefings or follow Commander's Intent. When a small fire threatens a low-priority outpost, the heuristic will blindly divert all resources to save it—abandoning the Priority 1 city to a massive inferno. Our RL agent reads the briefing, understands the Commander's priorities, and makes the hard strategic tradeoffs required in disaster management."
-
-## Proposed Changes
-
-We will make the following code adjustments to guarantee the heuristic fails in specific, explainable ways, while the RL agent is incentivized to succeed:
-
-### 1. Introduce Resource Scarcity (`env/models.py`)
-Currently, the heuristic has enough crews and firebreak budget to surround *everything*. By slightly reducing these budgets on `medium` and `hard` tiers, the agent *must* prioritize. 
-- **Modify `TIER_MEDIUM`**: Reduce crews from 5 to 4, firebreaks from 20 to 15.
-- **Modify `TIER_HARD`**: Reduce crews from 6 to 5, firebreaks from 30 to 20.
-
-### 2. Heavily Penalize Priority Zone Loss (`env/reward.py`)
-The `OperationalBriefing` defines `priority_populated_zones`. Right now, the reward gives a small +1.0 terminal bonus if they survive. We will change this to be a massive penalty if they burn.
-- **Terminal Reward**: If any `priority_populated_zone` burns, apply a `-5.0` penalty. 
-- **Step Reward**: If the population lost belongs to a priority zone, apply a much harsher delta penalty. This ensures the heuristic's score tanks when it ignores the briefing.
-
-### 3. Create the "Decoy" Ignition (`env/wildfire_env.py`)
-In `_ignite_initial_fires`, when there are multiple ignitions (medium/hard), we will ensure one ignition is closer to a *non-priority* zone, and one is slightly further from a *priority* zone. 
-- Because the heuristic purely sorts by `Manhattan distance to fire` in `_protect_population`, it will take the bait and commit its limited crews to the non-priority zone. 
-- The RL agent, reading the prompt, will learn to route crews to the priority zone first.
-
-### 4. Remove the Heuristic's "Omniscience" (`agents/heuristic_agent.py`)
-The heuristic currently has a few "cheat" behaviors where it perfectly calculates the safest deployment without needing recon. We will slightly dumb down `_initial_deployment` so it spreads crews out blindly, forcing it to actually rely on `RECON_FLIGHT` to find fires, wasting valuable early steps that the RL agent can optimize.
-
-## User Review Required
-Do you approve of this "Commander's Intent / Decoy Fire" strategy? It preserves the heuristic's strength in easy scenarios but guarantees it fails in complex scenarios, making your RL training the obvious hero of the presentation.

sft_final.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e5d8e965e5ff2910ceed81cd241199eea976e157373dc899f9273f009306ce62
-size 76858791

yt.txt

@@ -1,47 +0,0 @@
-Shot-by-Shot Script
-[0:00–0:08] — COLD OPEN (Hook)
-[SHOT: A real life wildfire is shown spreading (potentially displaying humans trying to deal with it, this transitions to a GIF replay of wildfire spreading across the grid, crews moving based on our application]
-VO: "Wildfire spreading. Civilian zones at risk. Limited resources. No second chances."
-[SHOT: Title card fades in — "Wildfire Containment Simulator"]
-VO: "What if an AI could be trained to handle this?"
-
-[0:08–0:20] — Problem Statement
-[SHOT: Simple diagram — grid with fire cells, population zones, crew icons]
-VO: "We built a fully-featured wildfire simulation for the OpenEnv hackathon. An LLM acts as Incident Commander — reading operational briefings, dispatching fire crews, calling air tankers, and building firebreaks — all in real-time, across a 300-step episode."
-
-[0:20–0:38] — The Environment
-[SHOT: Code snippet — env.reset("hard", seed=42) → GIF of Hard tier 40×40 grid]
-VO: "Three difficulty tiers: Easy is a 15x15 flatland fire. Hard is a 40x40 wildland-urban interface with fog-of-war, staggered ignitions, and mid-episode crew casualties."
-[SHOT: Annotated grid showing smoke, fog-of-war, crew positions, fire clusters]
-VO: "Fire spreads using a Rothermel-inspired model — wind direction, slope, fuel load, moisture all matter. The agent only sees what's not hidden by smoke."
-
-[0:38–0:55] — The Actions & Observations
-[SHOT: Terminal showing serialized observation text prompt scrolling]
-VO: "Each step, the observation is converted into a structured text prompt — fire cluster locations, resource status, recent events. The model responds with a single JSON action."
-[SHOT: JSON action example on screen — {"action_type": "drop_retardant", "tanker_id": "tanker_0", "target_row": 12, "target_col": 8}]
-VO: "Six action types: deploy crews, move them, drop retardant, build firebreaks, recon flight, or idle. Invalid JSON? The 3-layer parser falls back to safe idle — the loop never breaks."
-
-[0:55–1:12] — Training with GRPO
-[SHOT: Training curve plot — reward vs steps, tier promotion markers visible]
-VO: "We trained Qwen-2.5-1.5B using GRPO — Group Relative Policy Optimization — on a T4 GPU. The reward is decomposed: dense per-step signals for containment and population safety, plus a sparse +5 bonus if all civilians survive."
-[SHOT: Curriculum diagram: Easy → Medium → Hard with arrows]
-VO: "A curriculum controller automatically promotes the agent as it improves — just like training a real firefighter."
-
-[1:12–1:35] — Results
-[SHOT: Comparison table — Random / Heuristic / Trained LLM across three tiers]
-VO: "Our trained model reaches a mean reward of ~4.8 on Easy and ~3.9 on Hard — approaching the hand-coded heuristic baseline, without a single line of explicit strategy. Just reward signal."
-[SHOT: Side-by-side GIF replay — heuristic agent vs. trained LLM on medium tier]
-VO: "The model learns to protect population zones first, use tankers selectively, and build firebreaks ahead of the spread — behaviors we never explicitly programmed."
-
-[1:35–1:50] — Architecture Highlights
-[SHOT: Architecture diagram — env → serializer → LLM → parser → env]
-VO: "Built on Pydantic for typed validation end-to-end. Exposed as a FastAPI REST server so any model can plug in with zero Python imports. Fully OpenEnv compliant."
-[SHOT: HuggingFace Space page]
-VO: "Live on HuggingFace Spaces right now."
-
-[1:50–2:00] — Close
-[SHOT: Final GIF — fire successfully contained, population zones intact]
-VO: "Long-horizon planning. Instruction following. Curriculum learning. All in 1.5 billion parameters."
-[SHOT: Title card + GitHub/HF links]
-VO: "Wildfire Containment Simulator — OpenEnv Hackathon, Theme 2."
-