Initial commit

.gitattributes

@@ -29,3 +29,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard 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 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 1775.25 +/- 223.91
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+---
+
+# **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 sha256:39a6815b952a1f4a8351b6d6969ea5fc74e65e817810dba5bae46a30d7ccc645
+size 129189

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,105 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f616e8f1680>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f616e8f1710>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f616e8f17a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f616e8f1830>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f616e8f18c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f616e8f1950>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f616e8f19e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f616e8f1a70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f616e8f1b00>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f616e8f1b90>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f616e8f1c20>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f616e93ca80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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": 1659278352.7898135,
+    "learning_rate": 0.00096,
+    "tensorboard_log": "./tensorboard",
+    "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="
+    },
+    "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_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 sha256:2f51ab31bb6782c00f49270f76cbe019fd6fafd535fcf80c217042cc0e4cc6b3
+size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:337c3e54080e75a1d372b477f73cf3eae77fead087570eed700723db9e3fb2a0
+size 56766

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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f616e8f1680>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f616e8f1710>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f616e8f17a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f616e8f1830>", "_build": "<function ActorCriticPolicy._build at 0x7f616e8f18c0>", "forward": "<function ActorCriticPolicy.forward at 0x7f616e8f1950>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f616e8f19e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f616e8f1a70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f616e8f1b00>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f616e8f1b90>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f616e8f1c20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f616e93ca80>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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": 1659278352.7898135, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="}, "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "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 sha256:169d671ce47582e94140c89e0318ce29a6e364156d74048f7f1b2d28f7e51672
+size 1111187

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1775.2491272241343, "std_reward": 223.90735068875637, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-31T16:00:14.448608"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f14e6fdcec652ca8675ff9476b2128b5af909ce9b56d4cb4cd65e3fd42486385
+size 2763