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: 1495.90 +/- 315.48
+      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 sha256:5bb6e67f6afde225547094af4ca4f34dc4cb9fcbbd161ad88fc3bb89c3a1fc5d
+size 129261

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 0x7f9bc54e9ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9bc54e9f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9bc54ec040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9bc54ec0d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9bc54ec160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9bc54ec1f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9bc54ec280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9bc54ec310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9bc54ec3a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9bc54ec430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9bc54ec4c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9bc54ec550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f9bc54eaa40>"
+    },
+    "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": 1679551722489874639,
+    "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:c96470db629060e4de88d589c0185f96a1dc9915eb90474e6b4cd4d308f30f18
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:caf9ba1f8fd01745ac6a70c9a1c9554454c3335778427a92dd85424877b3585d
+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 0x7f9bc54e9ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9bc54e9f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9bc54ec040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9bc54ec0d0>", "_build": "<function ActorCriticPolicy._build at 0x7f9bc54ec160>", "forward": "<function ActorCriticPolicy.forward at 0x7f9bc54ec1f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9bc54ec280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9bc54ec310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9bc54ec3a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9bc54ec430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9bc54ec4c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9bc54ec550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9bc54eaa40>"}, "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": 1679551722489874639, "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-glibc2.31 # 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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:740363e8d021a5d7db8f12173cebb5c11dd5ad62bfda171a7c81c50c378d7d66
+size 1150239

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1495.898562303395, "std_reward": 315.4757978950735, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-23T07:09:10.804193"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fcefeda975e0d6564a0bb01872062b928a8cf4af5095068be8b92c6867a2679f
+size 2136