First model
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ppo-BipedalWalker-v3-test.zip +3 -0
- ppo-BipedalWalker-v3-test/_stable_baselines3_version +1 -0
- ppo-BipedalWalker-v3-test/data +99 -0
- ppo-BipedalWalker-v3-test/policy.optimizer.pth +3 -0
- ppo-BipedalWalker-v3-test/policy.pth +3 -0
- ppo-BipedalWalker-v3-test/pytorch_variables.pth +3 -0
- ppo-BipedalWalker-v3-test/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
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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: PPO
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results:
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- metrics:
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- type: mean_reward
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value: 80.95 +/- 9.81
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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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# **PPO** Agent playing **BipedalWalker-v3**
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This is a trained model of a **PPO** 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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config.json
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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. 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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f45e950add0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f45e950ae60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f45e950aef0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f45e950af80>", "_build": "<function 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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 0x7f45e950add0>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7f45e9510200>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f45e9510290>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f45e95103b0>",
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ppo-BipedalWalker-v3-test/policy.optimizer.pth
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OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
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{"mean_reward": 80.9542779488299, "std_reward": 9.812950029532301, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-17T18:15:44.350467"}
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