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  1. README.md +16 -40
  2. replay.mp4 +0 -0
  3. results.json +1 -1
README.md CHANGED
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  ---
 
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  tags:
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  - LunarLander-v2
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- - ppo
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  - deep-reinforcement-learning
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  - reinforcement-learning
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- - custom-implementation
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- - deep-rl-course
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  model-index:
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  - name: PPO
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  results:
@@ -17,45 +16,22 @@ model-index:
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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- value: -132.33 +/- 109.31
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  name: mean_reward
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  verified: false
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  ---
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- # PPO Agent Playing LunarLander-v2
 
 
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- This is a trained model of a PPO agent playing LunarLander-v2.
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-
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- # Hyperparameters
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- ```python
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- {'exp_name': 'ppo'
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- 'seed': 1
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- 'torch_deterministic': True
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- 'cuda': True
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- 'track': False
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- 'wandb_project_name': 'cleanRL'
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- 'wandb_entity': None
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- 'capture_video': False
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- 'env_id': 'LunarLander-v2'
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- 'total_timesteps': 50000
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- 'learning_rate': 0.00025
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- 'num_envs': 4
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- 'num_steps': 128
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- 'anneal_lr': True
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- 'gae': True
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- 'gamma': 0.99
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- 'gae_lambda': 0.95
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- 'num_minibatches': 4
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- 'update_epochs': 4
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- 'norm_adv': True
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- 'clip_coef': 0.2
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- 'clip_vloss': True
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- 'ent_coef': 0.01
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- 'vf_coef': 0.5
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- 'max_grad_norm': 0.5
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- 'target_kl': None
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- 'repo_id': 'Pandita-IA/ppo-LunarLander-v2'
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- 'batch_size': 512
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- 'minibatch_size': 128}
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- ```
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-
 
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  ---
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+ library_name: stable-baselines3
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  tags:
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  - LunarLander-v2
 
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  - deep-reinforcement-learning
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  - reinforcement-learning
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+ - stable-baselines3
 
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  model-index:
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  - name: PPO
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  results:
 
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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+ value: 276.93 +/- 17.41
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  name: mean_reward
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  verified: false
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  ---
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+ # **PPO** Agent playing **LunarLander-v2**
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+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+ ## Usage (with Stable-baselines3)
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+ TODO: Add your code
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+
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+
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+ ```python
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+ from stable_baselines3 import ...
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+ from huggingface_sb3 import load_from_hub
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+
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+ ...
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+ ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
replay.mp4 CHANGED
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results.json CHANGED
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- {"env_id": "LunarLander-v2", "mean_reward": -132.3282321866131, "std_reward": 109.3057635570201, "n_evaluation_episodes": 10, "eval_datetime": "2024-06-26T07:03:11.313173"}
 
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+ {"mean_reward": 276.9320266870259, "std_reward": 17.406463605708343, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-21T11:50:49.915166"}