Reinforcement Learning
stable-baselines3
TensorBoard
ppo
deep-reinforcement-learning
custom-implementation
deep-rl-course
LunarLander-v2
LunarLander-v3
Eval Results (legacy)
Instructions to use dawnandscience/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use dawnandscience/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="dawnandscience/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
Hyperparameters
{'exp_name': 'ppo_cartpole'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'dawnandscience/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
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Evaluation results
- mean_reward on CartPole-v1self-reported148.20 +/- 62.43