Upload SAC BipedalWalker-v3 trained agent
Browse files- .gitattributes +1 -0
- README.md +28 -0
- SAC-Mlp.zip +3 -0
- SAC-Mlp/_stable_baselines3_version +1 -0
- SAC-Mlp/actor.optimizer.pth +3 -0
- SAC-Mlp/critic.optimizer.pth +3 -0
- SAC-Mlp/data +118 -0
- SAC-Mlp/ent_coef_optimizer.pth +3 -0
- SAC-Mlp/policy.pth +3 -0
- SAC-Mlp/pytorch_variables.pth +3 -0
- SAC-Mlp/system_info.txt +7 -0
- config.json +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
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@@ -25,3 +25,4 @@ 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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*.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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README.md
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---
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library_name: stable-baselines3
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tags:
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- BipedalWalker-v3
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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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- metrics:
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- type: mean_reward
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value: 305.56 +/- 0.59
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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: BipedalWalker-v3
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type: BipedalWalker-v3
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---
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# **SAC** Agent playing **BipedalWalker-v3**
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This is a trained model of a **SAC** agent playing **BipedalWalker-v3** 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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SAC-Mlp.zip
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version https://git-lfs.github.com/spec/v1
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SAC-Mlp/_stable_baselines3_version
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1.5.0
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SAC-Mlp/actor.optimizer.pth
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SAC-Mlp/critic.optimizer.pth
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SAC-Mlp/data
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{
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"policy_class": {
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"__module__": "stable_baselines3.sac.policies",
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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 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()`` 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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"__init__": "<function SACPolicy.__init__ at 0x7f1542924550>",
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"_build": "<function SACPolicy._build at 0x7f15429245e0>",
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"policy_kwargs": {
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OS: Linux-5.13.0-44-generic-x86_64-with-glibc2.31 #49~20.04.1-Ubuntu SMP Wed May 18 18:44:28 UTC 2022
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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()`` 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 ", "__init__": "<function SACPolicy.__init__ at 0x7f1542924550>", "_build": "<function SACPolicy._build at 0x7f15429245e0>", "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 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{"mean_reward": 305.5584984690533, "std_reward": 0.5905645039328623, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-04T21:27:49.392117"}
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