---
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! 🚶‍♂️