Initial commit
Browse files- README.md +36 -0
- a2c-AntBulletEnv-v0.zip +3 -0
- a2c-AntBulletEnv-v0/_stable_baselines3_version +1 -0
- a2c-AntBulletEnv-v0/data +105 -0
- a2c-AntBulletEnv-v0/policy.optimizer.pth +3 -0
- a2c-AntBulletEnv-v0/policy.pth +3 -0
- a2c-AntBulletEnv-v0/pytorch_variables.pth +3 -0
- a2c-AntBulletEnv-v0/system_info.txt +7 -0
- config.json +1 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
README.md
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---
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library_name: stable-baselines3
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tags:
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- AntBulletEnv-v0
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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: 233.30 +/- 154.48
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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: AntBulletEnv-v0
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type: AntBulletEnv-v0
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---
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# **A2C** Agent playing **AntBulletEnv-v0**
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This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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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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a2c-AntBulletEnv-v0.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:4f2306c3827c88bf725aabac539694f0d98982cbb20f9159cbda1f09bcc6dddb
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size 124828
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a2c-AntBulletEnv-v0/_stable_baselines3_version
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1.6.0
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a2c-AntBulletEnv-v0/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 0x7fee25290830>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fee252908c0>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fee25290950>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fee252909e0>",
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"_build": "<function ActorCriticPolicy._build at 0x7fee25290a70>",
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"forward": "<function ActorCriticPolicy.forward at 0x7fee25290b00>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fee25290b90>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7fee25290c20>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fee25290cb0>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fee25290d40>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fee25290dd0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc_data object at 0x7fee2525ab40>"
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},
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"verbose": 1,
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"policy_kwargs": {
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":type:": "<class 'dict'>",
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"log_std_init": -2,
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"ortho_init": false,
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"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
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"optimizer_kwargs": {
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"alpha": 0.99,
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"eps": 1e-05,
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"weight_decay": 0
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}
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"dtype": "float32",
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],
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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 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\n False False False False]",
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},
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"n_envs": 4,
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"num_timesteps": 224,
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"_total_timesteps": 200,
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"tensorboard_log": "./tensorboard",
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},
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},
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"_n_updates": 7,
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"n_steps": 8,
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"gamma": 0.99,
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"gae_lambda": 0.9,
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"ent_coef": 0.0,
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"vf_coef": 0.4,
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"max_grad_norm": 0.5,
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"normalize_advantage": false
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}
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a2c-AntBulletEnv-v0/policy.optimizer.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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a2c-AntBulletEnv-v0/policy.pth
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a2c-AntBulletEnv-v0/pytorch_variables.pth
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a2c-AntBulletEnv-v0/system_info.txt
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OS: Darwin-19.6.0-x86_64-i386-64bit Darwin Kernel Version 19.6.0: Tue Feb 15 21:39:11 PST 2022; root:xnu-6153.141.59~1/RELEASE_X86_64
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Python: 3.7.6
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Stable-Baselines3: 1.6.0
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PyTorch: 1.11.0
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GPU Enabled: False
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config.json
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__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. 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results.json
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{"mean_reward": 233.3024806248024, "std_reward": 154.47920955008576, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-29T10:16:01.592776"}
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