Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.19 +/- 0.10
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-PandaReachDense-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c3a5205e94accfc6c5ae635b982c7185f2bd9636eb338725f095bf878e1513b
+size 106831

a2c-PandaReachDense-v3/_stable_baselines3_version

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

a2c-PandaReachDense-v3/data

@@ -0,0 +1,97 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f8e3b21ba30>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f8e3b221900>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1692222034829636206,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.27113327  0.01238791  0.45032892]\n [ 0.27113327  0.01238791  0.45032892]\n [-0.81170255  2.7777028  -2.2691593 ]\n [-0.06433139  0.4325571  -0.18560739]]",
+        "desired_goal": "[[ 0.8349881   1.4587048   0.01053923]\n [ 0.8913939   0.11732314 -0.9183379 ]\n [-0.03431995  1.5373065  -0.2589858 ]\n [-1.4662657   1.2522238  -0.24177454]]",
+        "observation": "[[ 0.27113327  0.01238791  0.45032892  0.4570533  -0.00648886  0.38646433]\n [ 0.27113327  0.01238791  0.45032892  0.4570533  -0.00648886  0.38646433]\n [-0.81170255  2.7777028  -2.2691593  -0.432184    0.15190867  1.4609872 ]\n [-0.06433139  0.4325571  -0.18560739 -1.8175007   1.6759236  -1.4155809 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[-0.03176781  0.0266825   0.01905715]\n [-0.09379778  0.09833882  0.23280142]\n [-0.10403973 -0.13055365  0.07189419]\n [ 0.00526707  0.10804585  0.1265759 ]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "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": 50000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35ded2c5da8e45018ffdbf84bc95c39b0846a3403be77f6870635c90b746ca55
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:358e64cc8a96fb8b563469716addad52e7e84571cd05a04016fc6f05550ccb14
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

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

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f8e3b21ba30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f8e3b221900>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1692222034829636206, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.27113327  0.01238791  0.45032892]\n [ 0.27113327  0.01238791  0.45032892]\n [-0.81170255  2.7777028  -2.2691593 ]\n [-0.06433139  0.4325571  -0.18560739]]", "desired_goal": "[[ 0.8349881   1.4587048   0.01053923]\n [ 0.8913939   0.11732314 -0.9183379 ]\n [-0.03431995  1.5373065  -0.2589858 ]\n [-1.4662657   1.2522238  -0.24177454]]", "observation": "[[ 0.27113327  0.01238791  0.45032892  0.4570533  -0.00648886  0.38646433]\n [ 0.27113327  0.01238791  0.45032892  0.4570533  -0.00648886  0.38646433]\n [-0.81170255  2.7777028  -2.2691593  -0.432184    0.15190867  1.4609872 ]\n [-0.06433139  0.4325571  -0.18560739 -1.8175007   1.6759236  -1.4155809 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[-0.03176781  0.0266825   0.01905715]\n [-0.09379778  0.09833882  0.23280142]\n [-0.10403973 -0.13055365  0.07189419]\n [ 0.00526707  0.10804585  0.1265759 ]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "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": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.1933919853530824, "std_reward": 0.09792238759595134, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-16T22:50:55.636529"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a3cd36b2ada70b0f55ea149b1fb2e86fb5182fe5356cc03900a0c1930903736
+size 2623