Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

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: 1328.21 +/- 226.21
+      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:b29f3a73ff454ddf9b68fde8798b2bad33a76cb55d28d86e5effe36d240376be
+size 129260

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 0x7f85fcb3dd30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f85fcb3ddc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f85fcb3de50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f85fcb3dee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f85fcb3df70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f85fcb41040>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f85fcb410d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f85fcb41160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f85fcb411f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f85fcb41280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f85fcb41310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f85fcb413a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f85fcb38a50>"
+    },
+    "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:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAEBAQEBAQEBlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1674035393267921885,
+    "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": 0.9,
+    "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 sha256:3f547e900cf61d93b70d3f3dc19d2741ed873dad502c404df78229055e0eee0f
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03d08e1750c23813233f9140f18916e0e08d9302bf1b9ab04c619abfb53a3b97
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7f85fcb3dd30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f85fcb3ddc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f85fcb3de50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f85fcb3dee0>", "_build": "<function ActorCriticPolicy._build at 0x7f85fcb3df70>", "forward": "<function ActorCriticPolicy.forward at 0x7f85fcb41040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f85fcb410d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f85fcb41160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f85fcb411f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f85fcb41280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f85fcb41310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f85fcb413a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f85fcb38a50>"}, "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": 1674035393267921885, "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": 0.9, "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0ba149fef8d844a5fd8f25e250818bb076933f8c1b61c78754f8df04b709b08
+size 1192516

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1328.2092687335796, "std_reward": 226.21222248706118, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T10:46:25.019401"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf3b57cdf6ac1e37b6ac1ddbc27bbfc155ea4b457c4459454f20db3ac49e3045
+size 2521