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: 2992.13 +/- 55.54
+      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 sha256:7fe167fa72a102631922c50e6c755640b932d69be9416b03234f512eeb672602
+size 130079

a2c-AntBulletEnv-v0/_stable_baselines3_version

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

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,109 @@
+{
+    "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 0x7ff06ed7c3a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff06ed7c430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff06ed7c4c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff06ed7c550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff06ed7c5e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff06ed7c670>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff06ed7c700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff06ed7c790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff06ed7c820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff06ed7c8b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff06ed7c940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff06ed7c9d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff06ed70f60>"
+    },
+    "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": 1674588743994659442,
+    "learning_rate": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "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:ae34a234a13e78e7764685d17296460fb4b030a1e520b8dddb06e1151b45c170
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8d436595959204150e7bb98d9534582e5b1f9994536c261ca00a695c7ce203c
+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 0x7ff06ed7c3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff06ed7c430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff06ed7c4c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff06ed7c550>", "_build": "<function ActorCriticPolicy._build at 0x7ff06ed7c5e0>", "forward": "<function ActorCriticPolicy.forward at 0x7ff06ed7c670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff06ed7c700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff06ed7c790>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff06ed7c820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff06ed7c8b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff06ed7c940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff06ed7c9d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff06ed70f60>"}, "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": 1674588743994659442, "learning_rate": {":type:": "<class 'function'>", ":serialized:": "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"}, "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 sha256:0de71db0bcf911265d18a06e59b957a8accaaab946bd53ea189534b13147d53b
+size 1229831

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2992.133482391329, "std_reward": 55.53945472831181, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-24T20:22:24.991332"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adc83dab34824d65de417927b064f0fa3cd5d5293d50d7bd695ab9ca36c1bee5
+size 2136