| 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: -76.70 +/- 25.91 | |
| name: mean_reward | |
| verified: false | |
| # PPO Agent Playing LunarLander-v2 | |
| This is my Hugging Face Deep Reinforcement Learning Course **Unit 8 Part 1** submission. | |
| The PPO agent was implemented from scratch with PyTorch and trained on the exact | |
| course environment, `LunarLander-v2`. | |
| ## Evaluation | |
| - Mean reward: **-76.70** | |
| - Standard deviation: **25.91** | |
| - Episodes: **20** | |
| ## Hyperparameters | |
| ```json | |
| { | |
| "env_id": "LunarLander-v2", | |
| "seed": 1, | |
| "total_timesteps": 500000, | |
| "learning_rate": 0.0003, | |
| "num_envs": 8, | |
| "num_steps": 256, | |
| "num_minibatches": 8, | |
| "update_epochs": 4, | |
| "gamma": 0.99, | |
| "gae_lambda": 0.95, | |
| "clip_coef": 0.2, | |
| "ent_coef": 0.01, | |
| "vf_coef": 0.5, | |
| "max_grad_norm": 0.5, | |
| "anneal_lr": true, | |
| "norm_adv": true, | |
| "clip_vloss": true, | |
| "target_kl": 0.03, | |
| "save_every_updates": 20, | |
| "resume": true | |
| } | |
| ``` | |