initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- Acrobot-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Acrobot-v1
+      type: Acrobot-v1
+    metrics:
+    - type: mean_reward
+      value: -78.00 +/- 8.97
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **Acrobot-v1**
+This is a trained model of a **PPO** agent playing **Acrobot-v1**
+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
+
+...
+```

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 0x000001BEB8DB1DA0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001BEB8DB1E40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001BEB8DB1EE0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001BEB8DB1F80>", "_build": "<function ActorCriticPolicy._build at 0x000001BEB8DB2020>", "forward": "<function ActorCriticPolicy.forward at 0x000001BEB8DB20C0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x000001BEB8DB2160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001BEB8DB2200>", "_predict": "<function ActorCriticPolicy._predict at 0x000001BEB8DB22A0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001BEB8DB2340>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001BEB8DB23E0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001BEB8DB2480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000001BEB8DAA600>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1003328, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689028829172559300, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0033279999999999976, "_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": 488, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVNAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWBgAAAAAAAAABAQEBAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLBoWUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgYAAAAAAAAAAQEBAQEBlGgVSwaFlGgZdJRSlIwGX3NoYXBllEsGhZSMA2xvd5RoESiWGAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/2w9JwdYx4sGUaAtLBoWUaBl0lFKUjARoaWdolGgRKJYYAAAAAAAAAAAAgD8AAIA/AACAPwAAgD/bD0lB1jHiQZRoC0sGhZRoGXSUUpSMCGxvd19yZXBylIxDWyAtMS4gICAgICAgIC0xLiAgICAgICAgLTEuICAgICAgICAtMS4gICAgICAgLTEyLjU2NjM3MSAtMjguMjc0MzM0XZSMCWhpZ2hfcmVwcpSMPVsgMS4gICAgICAgIDEuICAgICAgICAxLiAgICAgICAgMS4gICAgICAgMTIuNTY2MzcxIDI4LjI3NDMzNF2UjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True]", "_shape": [6], "low": "[ -1.        -1.        -1.        -1.       -12.566371 -28.274334]", "high": "[ 1.        1.        1.        1.       12.566371 28.274334]", "low_repr": "[ -1.        -1.        -1.        -1.       -12.566371 -28.274334]", "high_repr": "[ 1.        1.        1.        1.       12.566371 28.274334]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAwAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "3", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1028, "gamma": 0.9993, "gae_lambda": 0.99, "ent_coef": 0.0033, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVkQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAEsBSwFLE0MIlQGXAIkBUwCUToWUKYwBX5SFlIxdQzpcVXNlcnNcdm95elwudmlydHVhbGVudnNcZGVlcF9ybF9jb3Vyc2VcTGliXHNpdGUtcGFja2FnZXNcc3RhYmxlX2Jhc2VsaW5lczNcY29tbW9uXHV0aWxzLnB5lIwEZnVuY5RLhEMCBAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flGgNdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBdoDowMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBiMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Windows-10-10.0.22621-SP0 10.0.22621", "Python": "3.11.3", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.25.1", "Cloudpickle": "1.6.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.19.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -78.0, "std_reward": 8.966604708583958, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-10T23:36:02.654432"}

v_arcobot_A01.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86a8459b40d2430fe505b599fb35a7916d1391a7bbcd2e01b9ec42fb303ee227
+size 141713

v_arcobot_A01/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

v_arcobot_A01/data

@@ -0,0 +1,96 @@
+{
+    "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 0x000001BEB8DB1DA0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001BEB8DB1E40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001BEB8DB1EE0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001BEB8DB1F80>",
+        "_build": "<function ActorCriticPolicy._build at 0x000001BEB8DB2020>",
+        "forward": "<function ActorCriticPolicy.forward at 0x000001BEB8DB20C0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x000001BEB8DB2160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001BEB8DB2200>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x000001BEB8DB22A0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001BEB8DB2340>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001BEB8DB23E0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001BEB8DB2480>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x000001BEB8DAA600>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1003328,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1689028829172559300,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0033279999999999976,
+    "_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": 488,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True]",
+        "_shape": [
+            6
+        ],
+        "low": "[ -1.        -1.        -1.        -1.       -12.566371 -28.274334]",
+        "high": "[ 1.        1.        1.        1.       12.566371 28.274334]",
+        "low_repr": "[ -1.        -1.        -1.        -1.       -12.566371 -28.274334]",
+        "high_repr": "[ 1.        1.        1.        1.       12.566371 28.274334]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAwAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": "3",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 1028,
+    "gamma": 0.9993,
+    "gae_lambda": 0.99,
+    "ent_coef": 0.0033,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 32,
+    "n_epochs": 8,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

v_arcobot_A01/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f2d89ce09340138e45ce4adbb4e70759f277e8dfea3a5a368b3a13e1d4810ab
+size 85497

v_arcobot_A01/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:771b5f52db3374a02d93011dbfed5480d84af2ad8707a585e43969a467526d0f
+size 42049

v_arcobot_A01/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

v_arcobot_A01/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Windows-10-10.0.22621-SP0 10.0.22621
+- Python: 3.11.3
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.25.1
+- Cloudpickle: 1.6.0
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.19.0