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! 🚶‍♂️

Tanaybh/bipedal-walker-ppo

🤖 PPO/SAC Agent for BipedalWalker-v3

Model Description

Performance

Usage

Training Details

What Makes This Impressive

Tanaybh/
bipedal-walker-ppo