Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.19 +/- 0.08
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a8c3f87d484374853acbab68039cb4fd36c0ce6dc8a4f614934027eae994922
+size 106951

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.1.0

a2c-PandaReachDense-v3/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 0x7bb88080f880>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7bb8806242c0>"
+    },
+    "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": 1693748068879982964,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 2.6225021e-01 -8.7352941e-04  4.5422196e-01]\n [ 1.0634152e+00 -1.3119981e+00  8.3504312e-02]\n [-1.5416952e+00  8.5545027e-01  6.5538031e-01]\n [-1.2763584e-01 -4.7764927e-01 -2.2992367e-01]]",
+        "desired_goal": "[[-1.2057065   1.3499024   0.9340132 ]\n [ 1.1448876  -1.3733139   0.12418114]\n [-1.1329895   0.30412015  1.5344019 ]\n [-1.0624869  -0.93715006 -0.29288507]]",
+        "observation": "[[ 2.6225021e-01 -8.7352941e-04  4.5422196e-01  4.9190402e-01\n  -6.9236374e-03  3.8895217e-01]\n [ 1.0634152e+00 -1.3119981e+00  8.3504312e-02  1.1137964e+00\n  -9.1240376e-01  3.4868893e-01]\n [-1.5416952e+00  8.5545027e-01  6.5538031e-01 -9.5186120e-01\n  -1.7096411e-01  1.4632918e+00]\n [-1.2763584e-01 -4.7764927e-01 -2.2992367e-01 -1.8209695e+00\n  -1.6892402e+00 -1.3800822e+00]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.02632863 -0.01613657  0.216761  ]\n [-0.13234574 -0.14269115  0.23419611]\n [ 0.09116165 -0.02941206  0.16540833]\n [ 0.0473202   0.14259426  0.09744445]]",
+        "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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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a7832ad06ab30a39873517d2d9df521031402745b3743d7a227874d8f73ded6
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d1c84120b0be50e3b3fc92784dbfd45e8edbf6a3f854b00bda57a91622bbb9c
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

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

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.25.2

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 0x7bb88080f880>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7bb8806242c0>"}, "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": 1693748068879982964, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 2.6225021e-01 -8.7352941e-04  4.5422196e-01]\n [ 1.0634152e+00 -1.3119981e+00  8.3504312e-02]\n [-1.5416952e+00  8.5545027e-01  6.5538031e-01]\n [-1.2763584e-01 -4.7764927e-01 -2.2992367e-01]]", "desired_goal": "[[-1.2057065   1.3499024   0.9340132 ]\n [ 1.1448876  -1.3733139   0.12418114]\n [-1.1329895   0.30412015  1.5344019 ]\n [-1.0624869  -0.93715006 -0.29288507]]", "observation": "[[ 2.6225021e-01 -8.7352941e-04  4.5422196e-01  4.9190402e-01\n  -6.9236374e-03  3.8895217e-01]\n [ 1.0634152e+00 -1.3119981e+00  8.3504312e-02  1.1137964e+00\n  -9.1240376e-01  3.4868893e-01]\n [-1.5416952e+00  8.5545027e-01  6.5538031e-01 -9.5186120e-01\n  -1.7096411e-01  1.4632918e+00]\n [-1.2763584e-01 -4.7764927e-01 -2.2992367e-01 -1.8209695e+00\n  -1.6892402e+00 -1.3800822e+00]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.02632863 -0.01613657  0.216761  ]\n [-0.13234574 -0.14269115  0.23419611]\n [ 0.09116165 -0.02941206  0.16540833]\n [ 0.0473202   0.14259426  0.09744445]]", "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuDQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.1891912308521569, "std_reward": 0.07513004428221697, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-03T14:19:01.228997"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d64caaabb8ffa39294b32b3e96bffe64681f2850f08d86c20af12447c443c276
+size 2636