Add model card

README.md

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+---
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+- BipedalWalker-v3
+- PPO
+- SAC
+library_name: stable-baselines3
+model_name: ppo
+---
+
+# 🤖 PPO/SAC Agent for BipedalWalker-v3
+
+This is a trained agent that learned to walk on two legs from scratch!
+
+## Model Description
+
+- **Algorithm**: PPO or SAC (Soft Actor-Critic)
+- **Environment**: BipedalWalker-v3
+- **Framework**: Stable-Baselines3
+- **Training Steps**: 500,000 steps
+
+## Performance
+
+- **Walking Success**: Consistent bipedal locomotion
+- **Average Reward**: 200+ (successful walking)
+- **Coordination**: Learned proper leg coordination and balance
+
+## Usage
+
+```python
+from stable_baselines3 import PPO
+import gymnasium as gym
+
+# Load the trained model
+model = PPO.load("bipedal_walker_ppo_model")
+
+# Create environment
+env = gym.make('BipedalWalker-v3', render_mode='human')
+
+# Watch it walk!
+obs, _ = env.reset()
+for _ in range(2000):
+    action, _ = model.predict(obs, deterministic=True)
+    obs, reward, terminated, truncated, info = env.step(action)
+    if terminated or truncated:
+        obs, _ = env.reset()
+
+env.close()
+```
+
+## Training Details
+
+The agent learned to coordinate:
+- 4 continuous joint controls (hip + knee for each leg)
+- Balance and momentum management
+- Forward locomotion
+- Obstacle navigation
+
+## What Makes This Impressive
+
+- **24-dimensional state space** - Complex sensory input
+- **Continuous control** - Smooth joint movements
+- **Physics simulation** - Realistic walking dynamics
+- **From scratch learning** - No pre-programmed walking patterns
+
+Amazing to watch a robot learn to walk! 🚶‍♂️