| | --- |
| | tags: |
| | - LunarLander-v2 |
| | - ppo |
| | - deep-reinforcement-learning |
| | - reinforcement-learning |
| | - custom-implementation |
| | - deep-rl-course |
| | model-index: |
| | - name: PPO |
| | results: |
| | - task: |
| | type: reinforcement-learning |
| | name: reinforcement-learning |
| | dataset: |
| | name: LunarLander-v2 |
| | type: LunarLander-v2 |
| | metrics: |
| | - type: mean_reward |
| | value: -121.80 +/- 28.82 |
| | name: mean_reward |
| | verified: false |
| | --- |
| | |
| | # PPO Agent Playing LunarLander-v2 |
| | |
| | This is a trained model of a PPO agent playing LunarLander-v2. |
| | |
| | # Hyperparameters |
| | ```python |
| | exp_name: 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: 100000 |
| | 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: LizardAPN/LunarLander-v2-with-ppo |
| | batch_size: 512 |
| | minibatch_size: 128 |
| | ``` |
| | |
