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: 1603.57 +/- 83.38
+      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:ba0fccff00e177939f6a2fa47b87e61a136a932ff87722cc4b044b245e7cf54a
+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 0x7f2c241e2ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2c241e2d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2c241e2dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2c241e2e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2c241e2ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2c241e2f70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2c241e7040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2c241e70d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2c241e7160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2c241e71f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2c241e7280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2c241e7310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f2c241df570>"
+    },
+    "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": 1675362412884864790,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAGlUfr5up2a/8OGIPfurC0DhrmI/QvC2P1PAs75izTu/U8ewv0t+AL5j73a/4iIvu6u4YL834gtAmfQnP8EtxLx2nzi/oUgMQC9KQD9sZSU+rjslv6ZYB77wzGo/QlSXPhlhjb/JV9c+NuPSPkyvWT/blyA/Mnv4vgR9zT6v+xo/lQ2IvTjpjD7KUo+/IRwSv2c4oT6S8+G+DNXqPifYpD6kNEU+P38Sv2fJcD/gBoA7K71Avli2r791Kyu//gR4P3m7uD/pBsW7tTY2P0iDIb8Qxmc/yVfXPjbj0j6Uh5a/Z1xLP11dhb4ZbAI/WDnoP1GnOL/O+9G/IbmKv0jzlr/GFi8/EkqMv0pbir1eMx3AfL22vxOZJj3khAo/xHW+PhyNj7+aeSs/A0e/PmxbLL+JOac9OkW+vxLkGr/j96g/GWGNv8lX1z4249I+lIeWvy7AIT8gi96+0xfcPiQ/lz/f+aa+PfRmv0mumb4Mq4y/mN0yP41bKsD/3j4/R378vzDytr9cVZA7u+QgPk07PsBi3rK+E3UWv+b5+j6Vd7q/5r0sP2MAWT4BpGi+jb3vPxlhjb+mKhjANuPSPpSHlr+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"
+    },
+    "_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 sha256:49ff12d4d06b3fcc96e4f8161be24f268d85527952d94d9e889a0bbfe9babc7d
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5447a9268085ae121f3b4afa732e1e301dfa8bf8ec1885dec2cd93b865bffbf
+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 0x7f2c241e2ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2c241e2d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2c241e2dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2c241e2e50>", "_build": "<function ActorCriticPolicy._build at 0x7f2c241e2ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f2c241e2f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2c241e7040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2c241e70d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f2c241e7160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2c241e71f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2c241e7280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2c241e7310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f2c241df570>"}, "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": 1675362412884864790, "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 sha256:2489b98a87111256a495df34e2646e988178ad488986578dbac1bc0d2dd264d9
+size 1094773

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1603.573872421682, "std_reward": 83.3758825046396, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-02T19:30:05.468678"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c594c0c44d70f56e737a96fb678f34c52b9fc3b8edcc24b2d63e53882b4b3d8f
+size 2136