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: -0.22 +/- 0.10
+      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 sha256:38a797418576a227ceaf342860f5ddfe8a94646c9b0b14f1ee07df106c5fd264
+size 106915

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 0x78668013e050>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x786680131340>"
+    },
+    "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": 1692986235003233286,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-1.410179   -1.97277    -3.0035324 ]\n [ 0.24798748 -0.0048076   0.43963233]\n [ 0.5901921  -1.2322013   0.7834803 ]\n [ 0.24798748 -0.0048076   0.43963233]]",
+        "desired_goal": "[[ 0.10911665 -1.412374   -0.4712604 ]\n [ 0.46802002 -1.1859835  -0.5857424 ]\n [ 0.7430067  -1.565055    0.41632485]\n [ 0.32368994 -1.1717266   0.4257183 ]]",
+        "observation": "[[-1.4101790e+00 -1.9727700e+00 -3.0035324e+00  4.3561214e-01\n   7.6609500e-02 -2.6377720e-01]\n [ 2.4798748e-01 -4.8075952e-03  4.3963233e-01  4.4638550e-01\n  -1.9531988e-03  3.7256935e-01]\n [ 5.9019208e-01 -1.2322013e+00  7.8348029e-01  4.1394681e-01\n  -9.2783028e-01 -7.2318500e-01]\n [ 2.4798748e-01 -4.8075952e-03  4.3963233e-01  4.4638550e-01\n  -1.9531988e-03  3.7256935e-01]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.02196358  0.03637891  0.15789925]\n [ 0.00369854  0.06168788  0.15068424]\n [-0.09054084 -0.12920116  0.06069452]\n [-0.10875382 -0.00565503  0.23742737]]",
+        "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:edcd19b54601e3a06d19f3205fa3b01809e38661645b5f9bb2630972acb67662
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0eef811a00b8965987a8daf02b19709c47058b6bcc13985038466a745a5e6e4a
+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 0x78668013e050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x786680131340>"}, "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": 1692986235003233286, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAv4C0v7qD/L/gOUDAbvB9PgiJnbt9F+E+1BYXP8a4nb8qkkg/bvB9PgiJnbt9F+E+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAjXjfPazItL8LSfG+UqDvPk/Ol7838xW/sDU+P7lTyL+IKNU+sLqlPiP7lb/A99k+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAC/gLS/uoP8v+A5QMCOCN8+ceWcPc4Nh75u8H0+CImdu30X4T6kjOQ+PQEAu2nBvj7UFhc/xridvyqSSD/W8NM+SYZtv6ciOb9u8H0+CImdu30X4T6kjOQ+PQEAu2nBvj6UaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[-1.410179   -1.97277    -3.0035324 ]\n [ 0.24798748 -0.0048076   0.43963233]\n [ 0.5901921  -1.2322013   0.7834803 ]\n [ 0.24798748 -0.0048076   0.43963233]]", "desired_goal": "[[ 0.10911665 -1.412374   -0.4712604 ]\n [ 0.46802002 -1.1859835  -0.5857424 ]\n [ 0.7430067  -1.565055    0.41632485]\n [ 0.32368994 -1.1717266   0.4257183 ]]", "observation": "[[-1.4101790e+00 -1.9727700e+00 -3.0035324e+00  4.3561214e-01\n   7.6609500e-02 -2.6377720e-01]\n [ 2.4798748e-01 -4.8075952e-03  4.3963233e-01  4.4638550e-01\n  -1.9531988e-03  3.7256935e-01]\n [ 5.9019208e-01 -1.2322013e+00  7.8348029e-01  4.1394681e-01\n  -9.2783028e-01 -7.2318500e-01]\n [ 2.4798748e-01 -4.8075952e-03  4.3963233e-01  4.4638550e-01\n  -1.9531988e-03  3.7256935e-01]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.02196358  0.03637891  0.15789925]\n [ 0.00369854  0.06168788  0.15068424]\n [-0.09054084 -0.12920116  0.06069452]\n [-0.10875382 -0.00565503  0.23742737]]", "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": -0.2213854447938502, "std_reward": 0.10251040079930947, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-25T18:55:51.798413"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ba854cd5cc9bc61d3c7f2db8727293ac48e84ae8bd3654fe5a221c55f55ad1f
+size 2623