🤖 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
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! 🚶♂️
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