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: -0.25 +/- 0.12
+      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 sha256:63d2aa26e6ae5b3d60c8e4454c9d847413e1cdac2447d53c793b514675500ed1
+size 106831

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 0x7f2ed4b54820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f2ed4b4e100>"
+    },
+    "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": 1694751565696708281,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 1.2842255  -0.36587012 -0.7849975 ]\n [ 0.20070598  0.01455311  0.44115943]\n [ 0.20070598  0.01455311  0.44115943]\n [ 0.20070598  0.01455311  0.44115943]]",
+        "desired_goal": "[[ 1.4831083  -0.416562    0.4122545 ]\n [-1.2146599   0.46374658  0.7829076 ]\n [ 1.4598869   1.0263988  -0.42878073]\n [-0.12505224 -0.9213658   0.27416012]]",
+        "observation": "[[ 1.2842255  -0.36587012 -0.7849975   0.8663979   1.3282157  -0.01098592]\n [ 0.20070598  0.01455311  0.44115943  0.46891585  0.0020842   0.3830103 ]\n [ 0.20070598  0.01455311  0.44115943  0.46891585  0.0020842   0.3830103 ]\n [ 0.20070598  0.01455311  0.44115943  0.46891585  0.0020842   0.3830103 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.00414346 -0.12007459  0.20521727]\n [-0.00815818 -0.07853618  0.24940455]\n [-0.02635025  0.0341307   0.13728961]\n [-0.12180713  0.13797528  0.23928995]]",
+        "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:": "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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:128cbb92679886891651687d0475aa1c5e2e63ec21ac04e0232c5ef78b4921b9
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1f962644e66ccf898b41429b3f1861f2819cd07107f25f8581456c906ffe466
+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 0x7f2ed4b54820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f2ed4b4e100>"}, "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": 1694751565696708281, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 1.2842255  -0.36587012 -0.7849975 ]\n [ 0.20070598  0.01455311  0.44115943]\n [ 0.20070598  0.01455311  0.44115943]\n [ 0.20070598  0.01455311  0.44115943]]", "desired_goal": "[[ 1.4831083  -0.416562    0.4122545 ]\n [-1.2146599   0.46374658  0.7829076 ]\n [ 1.4598869   1.0263988  -0.42878073]\n [-0.12505224 -0.9213658   0.27416012]]", "observation": "[[ 1.2842255  -0.36587012 -0.7849975   0.8663979   1.3282157  -0.01098592]\n [ 0.20070598  0.01455311  0.44115943  0.46891585  0.0020842   0.3830103 ]\n [ 0.20070598  0.01455311  0.44115943  0.46891585  0.0020842   0.3830103 ]\n [ 0.20070598  0.01455311  0.44115943  0.46891585  0.0020842   0.3830103 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA48WHu6vp9b16JFI+5akFvJTXoL3oY38+fNzXvKHMCz2mlQw+BHb5vWRJDT5tCHU+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": "[[-0.00414346 -0.12007459  0.20521727]\n [-0.00815818 -0.07853618  0.24940455]\n [-0.02635025  0.0341307   0.13728961]\n [-0.12180713  0.13797528  0.23928995]]", "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:": "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"}, "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": -0.2524734754115343, "std_reward": 0.12119787568738824, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-15T05:03:52.203124"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03ea619486d5c5ef0f392647f9492ae7cf3a94f6a4a8875b203d7cb9ea07631c
+size 2623