Add detailed README with training config, results, and usage

README.md

@@ -16,22 +16,173 @@ model-index:
       type: LunarLander-v3
     metrics:
     - type: mean_reward
-      value: 254.32 +/- 14.40
+      value: 269.06 +/- 19.17
       name: mean_reward
       verified: false
 ---
 
-# **PPO** Agent playing **LunarLander-v3**
-This is a trained model of a **PPO** agent playing **LunarLander-v3**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+# PPO Agent — LunarLander-v3 🌕
 
-## Usage (with Stable-baselines3)
-TODO: Add your code
+A Proximal Policy Optimization (PPO) agent trained to land a spacecraft on the Moon using [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3) and [Gymnasium](https://gymnasium.farama.org/).
 
+> **Mean Reward: 269.06 ± 19.17** over 10 evaluation episodes — exceeds the 200-point solve threshold.
+
+---
+
+## Environment
+
+| Property | Value |
+|---|---|
+| Environment | `LunarLander-v3` |
+| Observation space | Box(8,) — position, velocity, angle, angular vel, leg contacts |
+| Action space | Discrete(4) — do nothing, fire left, fire main, fire right |
+| Solved threshold | ≥ 200 mean reward |
+
+### Reward breakdown
+- Closer to landing pad → higher reward
+- Slower movement → higher reward
+- Tilted angle → penalty
+- Each leg touching ground → +10
+- Side engine firing → −0.03/frame
+- Main engine firing → −0.3/frame
+- Crash → −100 | Safe landing → +100
+
+---
+
+## Training
+
+| Hyperparameter | Value |
+|---|---|
+| Algorithm | PPO |
+| Policy | MlpPolicy (2 × 64 Tanh layers) |
+| Total timesteps | 1,000,000 |
+| Parallel envs | 16 (vectorized) |
+| n_steps | 1024 |
+| batch_size | 64 |
+| n_epochs | 4 |
+| gamma | 0.999 |
+| gae_lambda | 0.98 |
+| ent_coef | 0.01 |
+| learning_rate | 3e-4 (default) |
+| Device | CPU |
+
+Training time: ~8 minutes on Apple M-series CPU.
+
+---
+
+## Results
+
+| Metric | Value |
+|---|---|
+| Mean reward (10 episodes) | **269.06** |
+| Std reward | ±19.17 |
+| Training timesteps | 1,000,000 |
+| Final ep_rew_mean (training) | ~268 |
+
+---
+
+## Usage
+
+### Load and run
+
+```python
+from stable_baselines3 import PPO
+from huggingface_sb3 import load_from_hub
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.monitor import Monitor
+import gymnasium as gym
+
+# Load from Hub
+checkpoint = load_from_hub(
+    repo_id="shivam3002/ppo-LunarLander-v3",
+    filename="ppo-LunarLander-v3.zip",
+)
+model = PPO.load(checkpoint)
+
+# Evaluate
+eval_env = Monitor(gym.make("LunarLander-v3", render_mode="human"))
+mean_reward, std_reward = evaluate_policy(
+    model, eval_env, n_eval_episodes=10, deterministic=True
+)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward:.2f}")
+eval_env.close()
+```
+
+### Render a single episode
 
 ```python
-from stable_baselines3 import ...
+import gymnasium as gym
+from stable_baselines3 import PPO
 from huggingface_sb3 import load_from_hub
 
-...
+checkpoint = load_from_hub("shivam3002/ppo-LunarLander-v3", "ppo-LunarLander-v3.zip")
+model = PPO.load(checkpoint)
+
+env = gym.make("LunarLander-v3", render_mode="human")
+obs, _ = env.reset()
+done = False
+total_reward = 0
+
+while not done:
+    action, _ = model.predict(obs, deterministic=True)
+    obs, reward, terminated, truncated, _ = env.step(action)
+    total_reward += reward
+    done = terminated or truncated
+
+print(f"Episode reward: {total_reward:.2f}")
+env.close()
+```
+
+---
+
+## Training code
+
+```python
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.monitor import Monitor
+import gymnasium as gym
+
+# Vectorized training env
+env = make_vec_env("LunarLander-v3", n_envs=16)
+
+model = PPO(
+    policy="MlpPolicy",
+    env=env,
+    n_steps=1024,
+    batch_size=64,
+    n_epochs=4,
+    gamma=0.999,
+    gae_lambda=0.98,
+    ent_coef=0.01,
+    verbose=1,
+)
+
+model.learn(total_timesteps=1_000_000)
+model.save("ppo-LunarLander-v3")
+
+# Evaluate
+eval_env = Monitor(gym.make("LunarLander-v3", render_mode="rgb_array"))
+mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward:.2f}")
+```
+
+---
+
+## Dependencies
+
+```
+gymnasium[box2d]>=1.0
+stable-baselines3>=2.0
+huggingface_sb3
+torch
 ```
+
+---
+
+## References
+
+- [Gymnasium LunarLander-v3 docs](https://gymnasium.farama.org/environments/box2d/lunar_lander/)
+- [Stable-Baselines3 PPO docs](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html)
+- [HuggingFace Deep RL Course — Unit 1](https://huggingface.co/deep-rl-course/unit1/introduction)