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: -1.97 +/- 0.70
+      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 sha256:402ea53f60b4738cec98bd3f52ddf9c3f79eb0265463646e221c754aee1be306
+size 108028

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7f16b9b44040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f16b9b432c0>"
+    },
+    "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
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679425823365392871,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.4470192  -0.00431717  0.54988486]\n [ 0.4470192  -0.00431717  0.54988486]\n [ 0.4470192  -0.00431717  0.54988486]\n [ 0.4470192  -0.00431717  0.54988486]]",
+        "desired_goal": "[[ 1.329305   -0.5427114   0.9474072 ]\n [ 0.616057    1.2431029   0.6829312 ]\n [-0.1638038   1.431445    1.3314116 ]\n [-0.73813885 -1.0763636   0.7281144 ]]",
+        "observation": "[[ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]\n [ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]\n [ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]\n [ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]]"
+    },
+    "_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": "[[ 0.14638753  0.02816411  0.21435887]\n [ 0.06400318 -0.05819974  0.19786857]\n [-0.01943633 -0.03319291  0.13169912]\n [-0.10458068 -0.07349382  0.05210426]]",
+        "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,
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f01eb3459051c7d1fd23bd64b0f52b8fa231226e3821107e6822c3bce45b85d2
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59a3e0657de5fb0cecff5bb8ef8a1a9ddefd24686dadb17ea0cbdd4cccfefdf1
+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 +1,7 @@
+- 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:": "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 0x7f16b9b44040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f16b9b432c0>"}, "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}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679425823365392871, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.4470192  -0.00431717  0.54988486]\n [ 0.4470192  -0.00431717  0.54988486]\n [ 0.4470192  -0.00431717  0.54988486]\n [ 0.4470192  -0.00431717  0.54988486]]", "desired_goal": "[[ 1.329305   -0.5427114   0.9474072 ]\n [ 0.616057    1.2431029   0.6829312 ]\n [-0.1638038   1.431445    1.3314116 ]\n [-0.73813885 -1.0763636   0.7281144 ]]", "observation": "[[ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]\n [ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]\n [ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]\n [ 0.4470192  -0.00431717  0.54988486  0.01013875 -0.00268392 -0.00397242]]"}, "_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": "[[ 0.14638753  0.02816411  0.21435887]\n [ 0.06400318 -0.05819974  0.19786857]\n [-0.01943633 -0.03319291  0.13169912]\n [-0.10458068 -0.07349382  0.05210426]]", "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, "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, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -1.9712886550463735, "std_reward": 0.6959720500024428, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-21T19:59:54.326582"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62685b977e9887e0f9714024b3fb594ae8dd8428c244ee513006cc7cbaba816c
+size 3056