Reinforcement Learning
stable-baselines3
BipedalWalker-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use chirbard/ppo-BipedalWalker-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use chirbard/ppo-BipedalWalker-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="chirbard/ppo-BipedalWalker-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
Hyperparameters
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1024,
batch_size = 64,
n_epochs = 4,
gamma = 0.99,
gae_lambda = 0.98,
ent_coef = 0.01,
verbose=1)
Train Time
Trained for 3 000 000 timesteps. Training took 1 hour and 8 minutes on Nvidia RTX A2000 Laptop.
- Downloads last month
- 5
Evaluation results
- mean_reward on BipedalWalker-v3self-reported264.50 +/- 2.61