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: 1387.05 +/- 203.84
+      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:acda310133ee60bcc770807711b86546f78a66601ad9830095c665d00b25ad56
+size 128976

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x7fa8bb6c08b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa8bb6c0940>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa8bb6c09d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa8bb6c0a60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa8bb6c0af0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa8bb6c0b80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa8bb6c0c10>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa8bb6c0ca0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa8bb6c0d30>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa8bb6c0dc0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa8bb6c0e50>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa8bb6c0ee0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa8bb6c2b40>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682449083810897719,
+    "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,
+    "_stats_window_size": 100,
+    "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,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cecb3320754b457480d4839d5c1460b13a0c3dce9e5b34878c1e4e4c374b9462
+size 56062

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:caa7787df5fae0985497d003a77a502442436493dcdc5f5aba4a928e11eea008
+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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: False
+- 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 0x7fa8bb6c08b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa8bb6c0940>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa8bb6c09d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa8bb6c0a60>", "_build": "<function ActorCriticPolicy._build at 0x7fa8bb6c0af0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa8bb6c0b80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa8bb6c0c10>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa8bb6c0ca0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa8bb6c0d30>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa8bb6c0dc0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa8bb6c0e50>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa8bb6c0ee0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa8bb6c2b40>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682449083810897719, "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, "_stats_window_size": 100, "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, "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, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9dcc3b944397efae3af3a83059db4cacc69cea818387eb69a9d2c94efef8376e
+size 1103089

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1387.0505916181944, "std_reward": 203.84115711478466, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-25T20:22:58.148003"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46272af69bb6390cf3082c70bc15c0b49294acf8aab16fe698bb95e5770513ed
+size 2170