Adding::Bipedal Walker trained using PPO
Browse files- README.md +37 -0
- bipedal_walker_10m.zip +3 -0
- bipedal_walker_10m/_stable_baselines3_version +1 -0
- bipedal_walker_10m/data +96 -0
- bipedal_walker_10m/policy.optimizer.pth +3 -0
- bipedal_walker_10m/policy.pth +3 -0
- bipedal_walker_10m/pytorch_variables.pth +3 -0
- bipedal_walker_10m/system_info.txt +7 -0
- config.json +1 -0
- results.json +1 -0
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: Proximal Policy Optimisation (PPO)
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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: BipedalWalker-v3
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type: BipedalWalker-v3
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metrics:
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- type: mean_reward
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value: 229.21 +/- 63.57
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name: mean_reward
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verified: false
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---
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# **Proximal Policy Optimisation (PPO)** Agent playing **BipedalWalker-v3**
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This is a trained model of a **Proximal Policy Optimisation (PPO)** agent playing **BipedalWalker-v3**
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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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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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bipedal_walker_10m.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:46711739cb5ae1c4562ba437e1c5926e8eeeafa39d1ca6c98d8a9a65ada12076
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size 173306
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bipedal_walker_10m/_stable_baselines3_version
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1.6.2
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bipedal_walker_10m/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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"__module__": "stable_baselines3.common.policies",
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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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"__init__": "<function ActorCriticPolicy.__init__ at 0x7fdcfd1cb5b0>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdcfd1cb640>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdcfd1cb6d0>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdcfd1cb760>",
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"_build": "<function ActorCriticPolicy._build at 0x7fdcfd1cb7f0>",
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"forward": "<function ActorCriticPolicy.forward at 0x7fdcfd1cb880>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdcfd1cb910>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7fdcfd1cb9a0>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdcfd1cba30>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdcfd1cbac0>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdcfd1cbb50>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7fdcfd1d4540>"
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},
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"verbose": 1,
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"policy_kwargs": {},
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"observation_space": {
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":type:": "<class 'gym.spaces.box.Box'>",
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"dtype": "float32",
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"_shape": [
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"high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf]",
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"bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]",
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"bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]",
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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()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. 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results.json
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@@ -0,0 +1 @@
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{"mean_reward": 229.21047812668112, "std_reward": 63.56924403610953, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-16T01:34:59.976281"}
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