SimingSiming
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upload ddpg BipedalWalkerHardcore-v3 trained agent
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ddpg_walker_v3.zip +3 -0
- ddpg_walker_v3/_stable_baselines3_version +1 -0
- ddpg_walker_v3/actor.optimizer.pth +3 -0
- ddpg_walker_v3/critic.optimizer.pth +3 -0
- ddpg_walker_v3/data +119 -0
- ddpg_walker_v3/policy.pth +3 -0
- ddpg_walker_v3/pytorch_variables.pth +3 -0
- ddpg_walker_v3/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
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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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*.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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- BipedalWalkerHardcore-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: DDPG
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results:
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- metrics:
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- type: mean_reward
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value: -122.85 +/- 24.22
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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: BipedalWalkerHardcore-v3
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type: BipedalWalkerHardcore-v3
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---
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# **DDPG** Agent playing **BipedalWalkerHardcore-v3**
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This is a trained model of a **DDPG** agent playing **BipedalWalkerHardcore-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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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnRkMy5wb2xpY2llc5SMCVREM1BvbGljeZSTlC4=", "__module__": "stable_baselines3.td3.policies", "__doc__": "\n Policy class (with both actor and critic) for TD3.\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 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 TD3Policy.__init__ at 0x7fc1a149e7a0>", "_build": "<function TD3Policy._build at 0x7fc1a149e830>", "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x7fc1a149e8c0>", "make_actor": "<function TD3Policy.make_actor at 0x7fc1a149e950>", "make_critic": "<function TD3Policy.make_critic at 0x7fc1a149e9e0>", "forward": "<function TD3Policy.forward at 0x7fc1a149ea70>", "_predict": "<function TD3Policy._predict at 0x7fc1a149eb00>", "set_training_mode": "<function TD3Policy.set_training_mode at 0x7fc1a149eb90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc1a14917b0>"}, "verbose": 1, "policy_kwargs": {"n_critics": 1}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": 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"__doc__": "\n Policy class (with both actor and critic) for TD3.\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 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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"_predict": "<function TD3Policy._predict at 0x7fc1a149eb00>",
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},
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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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Python: 3.7.13
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Stable-Baselines3: 1.5.0
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PyTorch: 1.11.0+cu113
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GPU Enabled: True
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{"mean_reward": -122.84601145910338, "std_reward": 24.21832344751911, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-09T22:10:31.132208"}
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