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ppo-seals-MountainCar-v0

Reinforcement Learning
stable-baselines3
seals/MountainCar-v0
deep-reinforcement-learning
Eval Results
Model card Files Community
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  1. .gitattributes +1 -0
  2. README.md +78 -0
  3. args.yml +79 -0
  4. config.yml +31 -0
  5. env_kwargs.yml +1 -0
  6. ppo-seals-MountainCar-v0.zip +3 -0
  7. ppo-seals-MountainCar-v0/_stable_baselines3_version +1 -0
  8. ppo-seals-MountainCar-v0/data +103 -0
  9. ppo-seals-MountainCar-v0/policy.optimizer.pth +3 -0
  10. ppo-seals-MountainCar-v0/policy.pth +3 -0
  11. ppo-seals-MountainCar-v0/pytorch_variables.pth +3 -0
  12. ppo-seals-MountainCar-v0/system_info.txt +7 -0
  13. replay.mp4 +3 -0
  14. results.json +1 -0
  15. train_eval_metrics.zip +3 -0
  16. vec_normalize.pkl +3 -0
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - seals/MountainCar-v0
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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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+ - metrics:
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+ - type: mean_reward
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+ value: -100.60 +/- 5.75
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+ name: mean_reward
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+ task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: seals/MountainCar-v0
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+ type: seals/MountainCar-v0
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+ ---
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+
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+ # **PPO** Agent playing **seals/MountainCar-v0**
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+ This is a trained model of a **PPO** agent playing **seals/MountainCar-v0**
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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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+
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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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+
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+ ## Usage (with SB3 RL Zoo)
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+
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+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+
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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m rl_zoo3.load_from_hub --algo ppo --env seals/MountainCar-v0 -orga ernestumorga -f logs/
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+ python enjoy.py --algo ppo --env seals/MountainCar-v0 -f logs/
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+ ```
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+
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+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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+ ```
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+ python -m rl_zoo3.load_from_hub --algo ppo --env seals/MountainCar-v0 -orga ernestumorga -f logs/
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+ rl_zoo3 enjoy --algo ppo --env seals/MountainCar-v0 -f logs/
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+ ```
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+
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+ ## Training (with the RL Zoo)
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+ ```
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+ python train.py --algo ppo --env seals/MountainCar-v0 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m rl_zoo3.push_to_hub --algo ppo --env seals/MountainCar-v0 -f logs/ -orga ernestumorga
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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', 512),
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+ ('clip_range', 0.2),
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+ ('ent_coef', 6.4940755116195606e-06),
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+ ('gae_lambda', 0.98),
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+ ('gamma', 0.99),
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+ ('learning_rate', 0.0004476103728105138),
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+ ('max_grad_norm', 1),
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+ ('n_envs', 16),
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+ ('n_epochs', 20),
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+ ('n_steps', 256),
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+ ('n_timesteps', 1000000.0),
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+ ('normalize', 'dict(norm_obs=False, norm_reward=True)'),
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+ ('policy',
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+ 'imitation.policies.base.MlpPolicyWithNormalizeFeaturesExtractor'),
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+ ('policy_kwargs',
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+ 'dict(activation_fn=nn.Tanh, net_arch=[dict(pi=[64, 64], vf=[64, '
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+ '64])])'),
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+ ('vf_coef', 0.25988158989488963),
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+ ('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})])
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+ ```
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