Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -0.70 +/- 0.20
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+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-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f848feb86abdd3a2c69954ed9f3a062c544e8ef1e440ec0d2381ae6776e2976
+size 106832

a2c-PandaReachDense-v2/_stable_baselines3_version

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

a2c-PandaReachDense-v2/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 0x7f24c0b82320>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f24c0b85740>"
+    },
+    "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": 1687946677060402285,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.4262574  -0.02358566  0.5735775 ]\n [ 0.4262574  -0.02358566  0.5735775 ]\n [ 0.4262574  -0.02358566  0.5735775 ]\n [ 0.4262574  -0.02358566  0.5735775 ]]",
+        "desired_goal": "[[-0.57126045  0.8594837  -0.97964996]\n [-0.5982568  -1.2898109  -1.1769137 ]\n [ 1.2996111   0.78581333 -0.14188986]\n [ 0.6907224   0.6263849   1.463662  ]]",
+        "observation": "[[ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]\n [ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]\n [ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]\n [ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.11092175 -0.03826596  0.08553431]\n [-0.0886913  -0.04666352  0.16203302]\n [ 0.14607269  0.08551623  0.09604799]\n [-0.14583607  0.03955791  0.06356456]]",
+        "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-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8333c688baa3b6cc82e8b487d12b2ca972b1ad26af2e1a73281fc583f2f9e6c8
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ccfc3fba769254e0a71ef28f44cd83ee3aa90fcbbc575b70cabea6b61166b24
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

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

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.21.0

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 0x7f24c0b82320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f24c0b85740>"}, "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": 1687946677060402285, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.4262574  -0.02358566  0.5735775 ]\n [ 0.4262574  -0.02358566  0.5735775 ]\n [ 0.4262574  -0.02358566  0.5735775 ]\n [ 0.4262574  -0.02358566  0.5735775 ]]", "desired_goal": "[[-0.57126045  0.8594837  -0.97964996]\n [-0.5982568  -1.2898109  -1.1769137 ]\n [ 1.2996111   0.78581333 -0.14188986]\n [ 0.6907224   0.6263849   1.463662  ]]", "observation": "[[ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]\n [ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]\n [ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]\n [ 0.4262574  -0.02358566  0.5735775   0.01078438 -0.00297931  0.00645319]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.11092175 -0.03826596  0.08553431]\n [-0.0886913  -0.04666352  0.16203302]\n [ 0.14607269  0.08551623  0.09604799]\n [-0.14583607  0.03955791  0.06356456]]", "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:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.704954834585078, "std_reward": 0.20231563372025996, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-28T11:00:02.862228"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b85f27f67659e3b6650aea7dc341bb7c65ae73cbbd4f63a25f6b6a379357ac32
+size 2601