Initial commit

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: 109.77 +/- 70.45
+      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 sha256:523ed2c147d8c8be4b4de270f2076544699e3bd7b749c813049eb0eea5e09d7e
+size 129058

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 0x7fb950d9e710>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb950d9e7a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb950d9e830>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb950d9e8c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb950d9e950>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb950d9e9e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb950d9ea70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb950d9eb00>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb950d9eb90>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb950d9ec20>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb950d9ecb0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb950d9ed40>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fb950d97640>"
+    },
+    "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:": "gAWVbQIAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWcAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lGgLSxyFlIwBQ5R0lFKUjARoaWdolGgTKJZwAAAAAAAAAAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UaAtLHIWUaBZ0lFKUjA1ib3VuZGVkX2JlbG93lGgTKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLHIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaCJLHIWUaBZ0lFKUjApfbnBfcmFuZG9tlE51Yi4=",
+        "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": 1679959760072894617,
+    "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 sha256:04b08b0764f4164ff6ce42ca2f3919a1af9894116f11808cfd95d6b2cd2d1089
+size 56062

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbe627e76ffd6d03b7e7d5ec47ef516843e78fbb8fe5e0704fc5541ed5db521c
+size 56830

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-5.19.0-35-generic-x86_64-with-glibc2.35 # 36~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 17 15:17:25 UTC 2
+- Python: 3.10.6
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu117
+- GPU Enabled: False
+- Numpy: 1.24.2
+- 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 0x7fb950d9e710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb950d9e7a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb950d9e830>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb950d9e8c0>", "_build": "<function ActorCriticPolicy._build at 0x7fb950d9e950>", "forward": "<function ActorCriticPolicy.forward at 0x7fb950d9e9e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb950d9ea70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb950d9eb00>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb950d9eb90>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb950d9ec20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb950d9ecb0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb950d9ed40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fb950d97640>"}, "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": 1679959760072894617, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV1wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMUi9ob21lL2FydGh1ci8ubG9jYWwvbGliL3B5dGhvbjMuMTAvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjFIvaG9tZS9hcnRodXIvLmxvY2FsL2xpYi9weXRob24zLjEwL3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9PdRBNVR1phZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_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-5.19.0-35-generic-x86_64-with-glibc2.35 # 36~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 17 15:17:25 UTC 2", "Python": "3.10.6", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0+cu117", "GPU Enabled": "False", "Numpy": "1.24.2", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 109.77118051938305, "std_reward": 70.45257329462649, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T21:16:16.342275"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0112dd490a4c3d31e7d7bd68cf08d3fdcf74330d351aeb982eb96ce3807f181a
+size 2136