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a2c-Acrobot-v1

Reinforcement Learning
stable-baselines3
Acrobot-v1
deep-reinforcement-learning
Eval Results
Model card Files Community
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  1. .gitattributes +2 -0
  2. README.md +59 -0
  3. a2c-Acrobot-v1.zip +3 -0
  4. a2c-Acrobot-v1/_stable_baselines3_version +1 -0
  5. a2c-Acrobot-v1/data +96 -0
  6. a2c-Acrobot-v1/policy.optimizer.pth +3 -0
  7. a2c-Acrobot-v1/policy.pth +3 -0
  8. a2c-Acrobot-v1/pytorch_variables.pth +3 -0
  9. a2c-Acrobot-v1/system_info.txt +7 -0
  10. args.yml +59 -0
  11. config.yml +11 -0
  12. env_kwargs.yml +1 -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
@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - Acrobot-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: A2C
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+ results:
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+ - metrics:
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+ - type: mean_reward
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+ value: -79.80 +/- 5.44
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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: Acrobot-v1
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+ type: Acrobot-v1
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+ ---
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+
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+ # **A2C** Agent playing **Acrobot-v1**
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+ This is a trained model of a **A2C** agent playing **Acrobot-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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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m utils.load_from_hub --algo a2c --env Acrobot-v1 -orga sb3 -f logs/
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+ python enjoy.py --algo a2c --env Acrobot-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 train.py --algo a2c --env Acrobot-v1 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m utils.push_to_hub --algo a2c --env Acrobot-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([('ent_coef', 0.0),
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+ ('n_envs', 16),
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+ ('n_timesteps', 500000.0),
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+ ('normalize', True),
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+ ('policy', 'MlpPolicy'),
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+ ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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+ ```
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+ "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\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 ",
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