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sac-seals-Humanoid-v1

Reinforcement Learning
stable-baselines3
seals/Humanoid-v1
deep-reinforcement-learning
Eval Results
Model card Files Community
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  1. .gitattributes +1 -0
  2. README.md +84 -0
  3. args.yml +81 -0
  4. config.yml +25 -0
  5. env_kwargs.yml +1 -0
  6. replay.mp4 +3 -0
  7. results.json +1 -0
  8. sac-seals-Humanoid-v1.zip +3 -0
  9. sac-seals-Humanoid-v1/_stable_baselines3_version +1 -0
  10. sac-seals-Humanoid-v1/actor.optimizer.pth +3 -0
  11. sac-seals-Humanoid-v1/critic.optimizer.pth +3 -0
  12. sac-seals-Humanoid-v1/data +129 -0
  13. sac-seals-Humanoid-v1/ent_coef_optimizer.pth +3 -0
  14. sac-seals-Humanoid-v1/policy.pth +3 -0
  15. sac-seals-Humanoid-v1/pytorch_variables.pth +3 -0
  16. sac-seals-Humanoid-v1/system_info.txt +9 -0
  17. train_eval_metrics.zip +3 -0
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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/Humanoid-v1
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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: SAC
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+ results:
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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/Humanoid-v1
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+ type: seals/Humanoid-v1
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+ metrics:
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+ - type: mean_reward
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+ value: 367.48 +/- 59.61
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **SAC** Agent playing **seals/Humanoid-v1**
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+ This is a trained model of a **SAC** agent playing **seals/Humanoid-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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+
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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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+ Install the RL Zoo (with SB3 and SB3-Contrib):
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+ ```bash
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+ pip install rl_zoo3
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+ ```
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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 sac --env seals/Humanoid-v1 -orga HumanCompatibleAI -f logs/
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+ python -m rl_zoo3.enjoy --algo sac --env seals/Humanoid-v1 -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 sac --env seals/Humanoid-v1 -orga HumanCompatibleAI -f logs/
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+ python -m rl_zoo3.enjoy --algo sac --env seals/Humanoid-v1 -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 -m rl_zoo3.train --algo sac --env seals/Humanoid-v1 -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 sac --env seals/Humanoid-v1 -f logs/ -orga HumanCompatibleAI
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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', 64),
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+ ('buffer_size', 100000),
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+ ('gamma', 0.98),
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+ ('learning_rate', 4.426351861707874e-05),
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+ ('learning_starts', 20000),
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+ ('n_timesteps', 2000000.0),
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+ ('policy', 'MlpPolicy'),
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+ ('policy_kwargs',
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+ {'log_std_init': -0.1034412732183072,
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+ 'net_arch': [400, 300],
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+ 'use_sde': False}),
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+ ('tau', 0.08),
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+ ('train_freq', 8),
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+ ('normalize', False)])
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+ ```
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+
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+ # Environment Arguments
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+ ```python
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+ {'render_mode': 'rgb_array'}
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+ ```
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+ !!python/object/apply:collections.OrderedDict
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+ - - - algo
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+ - sac
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+ - - conf_file
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+ - - device
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+ - cpu
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+ - false
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+ - - seed
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+ - - storage
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+ - null
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+ - - study_name
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+ - null
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+ - - tensorboard_log
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+ - ''
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+ - - track
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+ - false
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+ - - trained_agent
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+ - ''
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+ - - truncate_last_trajectory
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+ - true
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+ - - uuid
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+ - false
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+ - - vec_env
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+ - dummy
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+ - 400
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+ - - tau
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+ - 0.08
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+ - - train_freq
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+ - 8
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+ "__doc__": "\n Policy class (with both actor and critic) for SAC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
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