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README.md

@@ -9,16 +9,38 @@ app_port: 7860
 
 # Tetris OpenEnv
 
-A Tetris RL environment for LLM agent training, built on the OpenEnv spec.
+A Tetris RL environment for LLM agent training, built on OpenEnv 0.2.1.
 
 LLM agents receive a text-based board representation and must choose spatial actions (left, right, rotate, drop) to play Tetris. Features combo scoring where clearing multiple lines simultaneously gives disproportionately higher rewards.
 
-## API
+## Problem Statement
 
-- `POST /reset` — Start new episode, returns session_id + initial observation
-- `POST /step/{session_id}` — Take an action, returns observation + reward + done
-- `GET /state/{session_id}` — Get current state without acting
-- `GET /info` — Environment metadata
+**Wild Card (#5)** - Teaching LLMs spatial reasoning through Tetris. The agent must interpret a 2D text grid and plan piece placements, a fundamentally non-linguistic task solved through language.
+
+## Quick Start
+
+```python
+from tetris_env import TetrisEnvClient, TetrisAction
+
+with TetrisEnvClient(base_url="https://VortexedSquirrel-tetris-env.hf.space") as env:
+    result = env.reset(seed=42)
+    while not result.done:
+        action = TetrisAction(action="drop")
+        result = env.step(action)
+        print(f"Reward: {result.reward}, Score: {result.observation.score}")
+```
+
+## Actions
+
+| Action | Description |
+|---|---|
+| `left` | Move piece left |
+| `right` | Move piece right |
+| `rotate_cw` | Rotate clockwise |
+| `rotate_ccw` | Rotate counter-clockwise |
+| `drop` | Hard drop to bottom |
+| `down` | Soft drop one row |
+| `noop` | Do nothing |
 
 ## Reward Structure
 
@@ -30,3 +52,8 @@ LLM agents receive a text-based board representation and must choose spatial act
 | 4 (Tetris!) | +1500 | x15 |
 
 Penalties: -1/step, -2*height, -5*holes, -500 game over.
+
+## Built With
+
+- [OpenEnv 0.2.1](https://github.com/meta-pytorch/OpenEnv) by Meta PyTorch
+- Deployed on [Hugging Face Spaces](https://huggingface.co/spaces/VortexedSquirrel/tetris-env)