Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 827.63 +/- 90.48
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-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-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f113fa781aec9cd788296a4a758816789247a12a84015dae305dd948ea48261
+size 129265

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,106 @@
+{
+    "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 0x7fcd29ba0040>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcd29ba00d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcd29ba0160>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcd29ba01f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcd29ba0280>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcd29ba0310>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcd29ba03a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcd29ba0430>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcd29ba04c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcd29ba0550>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcd29ba05e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcd29ba0670>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fcd29b9ebc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "low": "[-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 -inf -inf -inf -inf]",
+        "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]",
+        "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]",
+        "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]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679874525576473816,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 62500,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8bf373f3f6463eeefe9159939ea6eb85b53c55ac85e217af649fb88f5dee5572
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0730a22587d25aab7258b8a7e23b2ca930c4f41a6d29be77474e4683f1a18ba
+size 56958

a2c-AntBulletEnv-v0/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"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 0x7fcd29ba0040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcd29ba00d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcd29ba0160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcd29ba01f0>", "_build": "<function ActorCriticPolicy._build at 0x7fcd29ba0280>", "forward": "<function ActorCriticPolicy.forward at 0x7fcd29ba0310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcd29ba03a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcd29ba0430>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcd29ba04c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcd29ba0550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcd29ba05e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcd29ba0670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fcd29b9ebc0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-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 -inf -inf -inf -inf]", "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]", "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]", "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]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679874525576473816, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 827.6313701999316, "std_reward": 90.4839493126437, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T01:01:46.704807"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3d1c88b8b10be6612bcbd3bd38b24d2639731049d3c8ae480c6b83fc5a7d4cc
+size 2136