Initial commit
Browse files- .gitattributes +1 -0
- README.md +37 -0
- config.json +1 -0
- hfc_a2c-AntBulletEnv-v0.zip +3 -0
- hfc_a2c-AntBulletEnv-v0/_stable_baselines3_version +1 -0
- hfc_a2c-AntBulletEnv-v0/data +107 -0
- hfc_a2c-AntBulletEnv-v0/policy.optimizer.pth +3 -0
- hfc_a2c-AntBulletEnv-v0/policy.pth +3 -0
- hfc_a2c-AntBulletEnv-v0/pytorch_variables.pth +3 -0
- hfc_a2c-AntBulletEnv-v0/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
- vec_normalize.pkl +3 -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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- 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: 1531.45 +/- 57.29
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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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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 0x7f7fc52fdab0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7fc52fdb40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7fc52fdbd0>", 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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 0x7f7fc52fdab0>",
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},
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":type:": "<class 'function'>",
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hfc_a2c-AntBulletEnv-v0/policy.optimizer.pth
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ADDED
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- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
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| 2 |
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oid sha256:f4b7db76d09ec218f7c7b5683f8e907dcf428f2cb335a8058883c8fcc9192f52
|
| 3 |
+
size 1169057
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 1531.4468487178906, "std_reward": 57.292831528297135, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-31T10:45:33.999515"}
|
vec_normalize.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5b1003d47eaa5bb5913d7644b3e3bf84b82e64468f96c2002b5ecf8dc7efb4b7
|
| 3 |
+
size 2176
|