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: -0.62 +/- 0.13
+      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 sha256:19f97fd5ecd8fb287655d99f2c9288f41cdd31fa7253e07af9193712da6a825f
+size 109512

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 0x7f6946c43ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f6946c442d0>"
+    },
+    "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": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1675265407706981419,
+    "learning_rate": 0.001,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/UGJN0vGp/IWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.39495355 -0.00521328  0.53626853]\n [ 0.39495355 -0.00521328  0.53626853]\n [ 0.39495355 -0.00521328  0.53626853]\n [ 0.39495355 -0.00521328  0.53626853]]",
+        "desired_goal": "[[ 1.5795072   0.47705862  1.5870938 ]\n [ 1.6707525   0.9896846  -0.84604293]\n [-0.49621093 -0.01013305 -0.6208378 ]\n [-0.6165255   1.4233006   1.0360899 ]]",
+        "observation": "[[ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]\n [ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]\n [ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]\n [ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]]"
+    },
+    "_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": "[[-0.10640336  0.04697625  0.27516234]\n [-0.14168139  0.03843269  0.07338439]\n [-0.06094106 -0.09585971  0.13096704]\n [-0.00238201  0.06245254  0.13013816]]",
+        "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": 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": 0.5,
+    "max_grad_norm": 1.0,
+    "normalize_advantage": true
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a4a31b620faaab431f5d8015cb8b39772a80489cc34e8b089272267fb19bf0a
+size 45438

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0aec581bd0bcd6d6b2d9d4f54229652675c5beb13c869e4022e2ad751e3241e2
+size 46718

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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7f6946c43ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6946c442d0>"}, "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": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675265407706981419, "learning_rate": 0.001, "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": "[[ 0.39495355 -0.00521328  0.53626853]\n [ 0.39495355 -0.00521328  0.53626853]\n [ 0.39495355 -0.00521328  0.53626853]\n [ 0.39495355 -0.00521328  0.53626853]]", "desired_goal": "[[ 1.5795072   0.47705862  1.5870938 ]\n [ 1.6707525   0.9896846  -0.84604293]\n [-0.49621093 -0.01013305 -0.6208378 ]\n [-0.6165255   1.4233006   1.0360899 ]]", "observation": "[[ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]\n [ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]\n [ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]\n [ 3.9495355e-01 -5.2132816e-03  5.3626853e-01 -1.5184259e-02\n  -4.2243878e-04 -1.2212385e-02]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAAerZvSxqQD0U4ow+7RQRvpprHT2OSpY9Vp15vRhSxL05HAY+dBscuzzOfz3wQgU+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==", "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": "[[-0.10640336  0.04697625  0.27516234]\n [-0.14168139  0.03843269  0.07338439]\n [-0.06094106 -0.09585971  0.13096704]\n [-0.00238201  0.06245254  0.13013816]]", "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": 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": 0.5, "max_grad_norm": 1.0, "normalize_advantage": true, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.6232797754637431, "std_reward": 0.1335224057209656, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-01T16:50:05.225225"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6826f803c91d4210c72eec9f158243907159294e79123e1211ad6389f60d0960
+size 3056