Initial Commit
Browse files- README.md +21 -1
- args.yml +7 -21
- ppo-CartPole-v1.zip +2 -2
- ppo-CartPole-v1/data +34 -33
- ppo-CartPole-v1/policy.optimizer.pth +1 -1
- ppo-CartPole-v1/policy.pth +1 -1
- replay.mp4 +2 -2
- results.json +1 -1
- train_eval_metrics.zip +3 -0
README.md
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@@ -23,7 +23,11 @@ model-index:
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# **PPO** Agent playing **CartPole-v1**
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This is a trained model of a **PPO** agent playing **CartPole-v1**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo)
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## Usage (with SB3 RL Zoo)
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# Upload the model and generate video (when possible)
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python -m utils.push_to_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3
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```
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# **PPO** Agent playing **CartPole-v1**
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This is a trained model of a **PPO** agent playing **CartPole-v1**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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The RL Zoo is a training framework for Stable Baselines3
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reinforcement learning agents,
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with hyperparameter optimization and pre-trained agents included.
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## Usage (with SB3 RL Zoo)
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# Upload the model and generate video (when possible)
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python -m utils.push_to_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3
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```
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+
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## Hyperparameters
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```python
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OrderedDict([('batch_size', 256),
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('clip_range', 'lin_0.2'),
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('ent_coef', 0.0),
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('gae_lambda', 0.8),
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('gamma', 0.98),
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('learning_rate', 'lin_0.001'),
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('n_envs', 8),
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('n_epochs', 20),
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('n_steps', 32),
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('n_timesteps', 100000.0),
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('policy', 'MlpPolicy'),
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('normalize', False)])
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```
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args.yml
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!!python/object/apply:collections.OrderedDict
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- - - algo
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- ppo
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-
- - device
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- auto
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- - env
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- CartPole-v1
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- ppo
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ppo-CartPole-v1.zip
CHANGED
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ppo-CartPole-v1/data
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