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: 1238.46 +/- 195.63
+      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 sha256:1deed56d169685f261822224b1e72c85fe953bc365931c02b909a3b0ce625e47
+size 129265

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 0x7fbf9499e3a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbf9499e430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbf9499e4c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbf9499e550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbf9499e5e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbf9499e670>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbf9499e700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbf9499e790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbf9499e820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbf9499e8b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbf9499e940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbf9499e9d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fbf949a1640>"
+    },
+    "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": 1680110457954562080,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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 sha256:5c538c4b9e721ad740613511fe67931f6a5f64868f536b9eec19a0c4ea50ce4c
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13e39e03fc95c9eb18893c250deae2a7f43e705f57162a5ba24cd671a4ca7159
+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 0x7fbf9499e3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbf9499e430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbf9499e4c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbf9499e550>", "_build": "<function ActorCriticPolicy._build at 0x7fbf9499e5e0>", "forward": "<function ActorCriticPolicy.forward at 0x7fbf9499e670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbf9499e700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbf9499e790>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbf9499e820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbf9499e8b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbf9499e940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbf9499e9d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fbf949a1640>"}, "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": 1680110457954562080, "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:a075d90d32efc0c0eb4ea9876b1104e95b250df4c3fd665f0ebe03ae5637b6f5
+size 1152958

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1238.4643345830962, "std_reward": 195.6286874573768, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-29T18:22:41.896362"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a8c61ebdfbfb0c6405828d700718173680e5b18945e978c2e713f4c53d3782e
+size 2136