CleanRL PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the CleanRL implementation with PyTorch.
Evaluation Results
- Mean Reward:
-182.76 +/- 119.89(evaluated over 10 episodes)
Hyperparameters
- exp_name:
cleanrl_ppo - seed:
1 - torch_deterministic:
True - cuda:
True - track:
False - wandb_project_name:
cleanRL - wandb_entity:
None - capture_video:
False - env_id:
LunarLander-v2 - total_timesteps:
50000 - learning_rate:
0.00025 - num_envs:
8 - 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:
Atharva1232/cleanrl-ppo-LunarLander-v2 - batch_size:
1024 - minibatch_size:
256
To learn more check Unit 8 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit8/introduction
Evaluation results
- mean_reward on LunarLander-v2self-reported-182.76 +/- 119.89