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README.md +36 -0
a2c-HalfCheetahBulletEnv-v0.zip +3 -0
a2c-HalfCheetahBulletEnv-v0/_stable_baselines3_version +1 -0
a2c-HalfCheetahBulletEnv-v0/data +105 -0
a2c-HalfCheetahBulletEnv-v0/policy.optimizer.pth +3 -0
a2c-HalfCheetahBulletEnv-v0/policy.pth +3 -0
a2c-HalfCheetahBulletEnv-v0/pytorch_variables.pth +3 -0
a2c-HalfCheetahBulletEnv-v0/system_info.txt +7 -0
config.json +1 -0
replay.mp4 +0 -0
results.json +1 -0
vec_normalize.pkl +3 -0

README.md ADDED Viewed

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+---
+library_name: stable-baselines3
+tags:
+- HalfCheetahBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 747.07 +/- 1132.58
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: HalfCheetahBulletEnv-v0
+      type: HalfCheetahBulletEnv-v0
+---
+# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-v0**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+## Usage (with Stable-baselines3)
+TODO: Add your code
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+...
+```

a2c-HalfCheetahBulletEnv-v0.zip ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35977eda28e308fee1afd441bf7d28d06e3feaf5998c2788f7a71e9fc1eb545c
+size 132121

a2c-HalfCheetahBulletEnv-v0/_stable_baselines3_version ADDED Viewed

	@@ -0,0 +1 @@


1	+ 1.6.0

a2c-HalfCheetahBulletEnv-v0/data ADDED Viewed

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+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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. 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 0x7f433e00b5f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f433e00b680>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f433e00b710>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f433e00b7a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f433e00b830>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f433e00b8c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f433e00b950>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f433e00b9e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f433e00ba70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f433e00bb00>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f433e00bb90>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f433e05c330>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
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+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
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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]",
+        "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]",
+        "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]",
+        "_np_random": "RandomState(MT19937)"
+    },
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+OS: Linux-5.10.133+-x86_64-with-debian-bullseye-sid #1 SMP Sat Sep 3 14:51:58 UTC 2022
+Python: 3.7.12
+Stable-Baselines3: 1.6.0
+PyTorch: 1.11.0
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

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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 0x7f433e00b5f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f433e00b680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f433e00b710>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f433e00b7a0>", "_build": "<function 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