Commit
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Parent(s):
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Initial commit
Browse files- .gitattributes +1 -0
- README.md +37 -0
- a2c-AntBulletEnv-v0.zip +3 -0
- a2c-AntBulletEnv-v0/_stable_baselines3_version +1 -0
- a2c-AntBulletEnv-v0/data +106 -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
- replay.mp4 +3 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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replay.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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- 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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- 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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metrics:
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- type: mean_reward
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value: 1264.54 +/- 56.63
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name: mean_reward
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verified: false
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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 sha256:52b1e5421db70afb1e901b0552702c3d1ec764aaa246e119e26363ddaa282f9b
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size 147279
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a2c-AntBulletEnv-v0/_stable_baselines3_version
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1.7.0
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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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| 4 |
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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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f4f106bc310>",
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| 8 |
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f106bc3a0>",
|
| 9 |
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f106bc430>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4f106bc4c0>",
|
| 11 |
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"_build": "<function ActorCriticPolicy._build at 0x7f4f106bc550>",
|
| 12 |
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"forward": "<function ActorCriticPolicy.forward at 0x7f4f106bc5e0>",
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4f106bc670>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f106bc700>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7f4f106bc790>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f106bc820>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f106bc8b0>",
|
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f106bc940>",
|
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc_data object at 0x7f4f106b48a0>"
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| 21 |
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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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":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
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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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},
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"observation_space": {
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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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"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\n False False False False]",
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"_np_random": null
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},
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"_shape": [
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"high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
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"bounded_below": "[ True True True True True True True True]",
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"bounded_above": "[ True True True True True True True True]",
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"_np_random": null
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},
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":type:": "<class 'function'>",
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a2c-AntBulletEnv-v0/policy.optimizer.pth
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ADDED
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size 431
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a2c-AntBulletEnv-v0/system_info.txt
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- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
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- Python: 3.8.10
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- Stable-Baselines3: 1.7.0
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- PyTorch: 1.13.1+cu116
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- GPU Enabled: True
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- Numpy: 1.21.6
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- Gym: 0.21.0
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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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f4f106bc310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f106bc3a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f106bc430>", 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replay.mp4
ADDED
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vec_normalize.pkl
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