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: -0.58 +/- 0.22
+      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 sha256:221c8037ddc99e4b752b618c0e5f2cbd8e27b965b48e2ea5b2a7c5984ba176fe
+size 109582

a2c-PandaReachDense-v2/_stable_baselines3_version

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

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 0x78482806f5b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78482806a5c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "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": 1689932019903916589,
+    "learning_rate": 0.00096,
+    "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": "[[ 0.30536717 -0.00991113  0.5879484 ]\n [ 0.30536717 -0.00991113  0.5879484 ]\n [ 0.30536717 -0.00991113  0.5879484 ]\n [ 0.30536717 -0.00991113  0.5879484 ]]",
+        "desired_goal": "[[-0.90334016  1.4576653  -0.22413738]\n [-1.6460264  -0.4076584  -0.1224539 ]\n [ 0.530114   -0.89746207  0.11915459]\n [-0.7709608  -1.1915708  -0.9236836 ]]",
+        "observation": "[[ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]\n [ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]\n [ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]\n [ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]]"
+    },
+    "_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////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAJc+9PUScIz2jqI0+2jqSPFFghbxhRDM+Zc4BPopJdD3zKqg9NNwxvaBYBT578Uo+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": "[[ 0.09268025  0.03994395  0.27667722]\n [ 0.01785033 -0.01628128  0.17506553]\n [ 0.1267639   0.05964044  0.08211317]\n [-0.04342289  0.13022089  0.1981868 ]]",
+        "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,
+    "_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": 31250,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "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 sha256:04dccdc7c4c281188d9c353cee65f982d95e4c04ce9c76ea8107f60da21aa881
+size 45438

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5f1f831b0dccbb9c638a153097dc3b3443d1456d9fc9ca8220674e40d0b890a
+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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+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 0x78482806f5b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78482806a5c0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "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": 1689932019903916589, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9PdRBNVR1phZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.30536717 -0.00991113  0.5879484 ]\n [ 0.30536717 -0.00991113  0.5879484 ]\n [ 0.30536717 -0.00991113  0.5879484 ]\n [ 0.30536717 -0.00991113  0.5879484 ]]", "desired_goal": "[[-0.90334016  1.4576653  -0.22413738]\n [-1.6460264  -0.4076584  -0.1224539 ]\n [ 0.530114   -0.89746207  0.11915459]\n [-0.7709608  -1.1915708  -0.9236836 ]]", "observation": "[[ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]\n [ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]\n [ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]\n [ 0.30536717 -0.00991113  0.5879484   0.08981374 -0.00064743  0.06686003]]"}, "_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": "[[ 0.09268025  0.03994395  0.27667722]\n [ 0.01785033 -0.01628128  0.17506553]\n [ 0.1267639   0.05964044  0.08211317]\n [-0.04342289  0.13022089  0.1981868 ]]", "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, "_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": 31250, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.5766107180621475, "std_reward": 0.21793575189336192, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-21T10:36:07.632097"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88527b8f6d4f032ea05e5bca805f0ede3f4f53a0caf6356400d4e58a5fc6d739
+size 2387