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: 1598.46 +/- 145.90
+      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:badd887f64ee825029958c35bd8c4b9423563c9e73aebc3270dbcd43de331ce9
+size 129005

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 0x7f25a3be11f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f25a3be1280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f25a3be1310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f25a3be13a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f25a3be1430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f25a3be14c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f25a3be1550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f25a3be15e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f25a3be1670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f25a3be1700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f25a3be1790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f25a3be1820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f25a3bdf390>"
+    },
+    "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": 1676490982372033465,
+    "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 sha256:82785114b95e61c05146aab806ec8137f66b8b20bc06fd45b3d3f6eda80c7d06
+size 56062

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47a3ab1d3f0fb1627b49e47ad2bd116f94151ecf2b586b2ab5203039b100bee8
+size 56830

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: False
+- 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 0x7f25a3be11f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f25a3be1280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f25a3be1310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f25a3be13a0>", "_build": "<function ActorCriticPolicy._build at 0x7f25a3be1430>", "forward": "<function ActorCriticPolicy.forward at 0x7f25a3be14c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f25a3be1550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f25a3be15e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f25a3be1670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f25a3be1700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f25a3be1790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f25a3be1820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f25a3bdf390>"}, "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": 1676490982372033465, "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": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aee7c78f79f5a604ee51498992c005e5d1c3f2ee8a88a09c7bc8df85b5f60c37
+size 1105684

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1598.4623135585803, "std_reward": 145.89993921162662, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-15T21:05:33.541165"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee8f2a5a7081e8c22f1172117856c803e9b5abc819c9fb3001b679f18c0d7d07
+size 2136