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: -0.88 +/- 0.18
+      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:cad57ea3509fb72fcd9684b8030343d02e2f9f2b8ed7ec8fdd7d05dee656f83c
+size 108130

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f0db9c444c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f0db9c45580>"
+    },
+    "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": 1682327358294300365,
+    "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": "[[0.4287463  0.00764228 0.55625236]\n [0.4287463  0.00764228 0.55625236]\n [0.4287463  0.00764228 0.55625236]\n [0.4287463  0.00764228 0.55625236]]",
+        "desired_goal": "[[ 0.3772525   0.26908398  0.5505531 ]\n [-0.8627287   1.3615875  -1.2244374 ]\n [ 1.3937244  -0.45431533  0.09036659]\n [ 0.77628976  1.4297385   0.11059314]]",
+        "observation": "[[ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]\n [ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]\n [ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]\n [ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]]"
+    },
+    "_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////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAw3q6PYxMOz0mXQ8+5wqLvWF8Bz6nTJU92ZdEvZ6/hr27oRk+LBngvHWLw7sbXJc+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": "[[ 0.09105446  0.0457273   0.14000377]\n [-0.06789189  0.1323104   0.07290011]\n [-0.04799638 -0.06579517  0.15003102]\n [-0.02735575 -0.00596755  0.29562458]]",
+        "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 '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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0b66af0005c4bbbde3e116743d731499ec5927b94927797c5e5aceb3c80de65
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d19e4cad1c059df4c17b4b796a87ed42c3c6497355c72a0341fb5949acd2ad17
+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: 1.8.0
+- PyTorch: 2.0.0+cu118
+- 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 0x7f0db9c444c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f0db9c45580>"}, "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": 1682327358294300365, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.4287463  0.00764228 0.55625236]\n [0.4287463  0.00764228 0.55625236]\n [0.4287463  0.00764228 0.55625236]\n [0.4287463  0.00764228 0.55625236]]", "desired_goal": "[[ 0.3772525   0.26908398  0.5505531 ]\n [-0.8627287   1.3615875  -1.2244374 ]\n [ 1.3937244  -0.45431533  0.09036659]\n [ 0.77628976  1.4297385   0.11059314]]", "observation": "[[ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]\n [ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]\n [ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]\n [ 4.2874631e-01  7.6422789e-03  5.5625236e-01 -4.3848632e-03\n  -3.3113190e-03  3.0444670e-04]]"}, "_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": "[[ 0.09105446  0.0457273   0.14000377]\n [-0.06789189  0.1323104   0.07290011]\n [-0.04799638 -0.06579517  0.15003102]\n [-0.02735575 -0.00596755  0.29562458]]", "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 '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, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.8826198122929781, "std_reward": 0.18340621488670825, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-24T10:13:26.841864"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4572c9c07ae27f6f09d82155d6c3b548c18c0dcb9d432bb72be265359b8edfe
+size 2381