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: -0.26 +/- 0.13
+      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_robot_arm.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13aafb051d73238136a361fce11b3daecf43c41eddf4d8b7b074473374bf6692
+size 106829

a2c_robot_arm/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0

a2c_robot_arm/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 0x7e6e40813010>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e6e4080ae00>"
+    },
+    "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": 500000,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691494772840529690,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.19383524  0.00195633  0.42790607]\n [ 0.19383524  0.00195633  0.42790607]\n [-0.11639074 -0.34614852 -0.13272111]\n [-0.5083086   0.33259195  0.33892584]]",
+        "desired_goal": "[[ 0.48405263 -1.0275805   0.6633325 ]\n [ 1.2858841  -0.33548224 -1.3393227 ]\n [-1.5286498  -1.0902944   0.03032325]\n [-0.17934647  1.1123023   0.5681949 ]]",
+        "observation": "[[ 0.19383524  0.00195633  0.42790607  0.43052822 -0.00497465  0.3592435 ]\n [ 0.19383524  0.00195633  0.42790607  0.43052822 -0.00497465  0.3592435 ]\n [-0.11639074 -0.34614852 -0.13272111 -1.8082856  -1.6978616  -1.3849537 ]\n [-0.5083086   0.33259195  0.33892584 -0.7858022   1.6569867   0.8502351 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.04863565  0.01680663  0.00884386]\n [-0.12578866  0.02681088  0.03398855]\n [-0.05419728 -0.08474252  0.0188862 ]\n [-0.09905962  0.09411886  0.2421644 ]]",
+        "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": 25000,
+    "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_robot_arm/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bad5eeba48cf23dd587a845e457a31b2df0d9741c329e2e97c449fad7a1cb4a9
+size 44734

a2c_robot_arm/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0026b7c6ed20707ce5ab66d77e6c810fb18a27e8195fef44d2a46b6b4ac750ca
+size 46014

a2c_robot_arm/pytorch_variables.pth

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

a2c_robot_arm/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: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- 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 0x7e6e40813010>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e6e4080ae00>"}, "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": 500000, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691494772840529690, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.19383524  0.00195633  0.42790607]\n [ 0.19383524  0.00195633  0.42790607]\n [-0.11639074 -0.34614852 -0.13272111]\n [-0.5083086   0.33259195  0.33892584]]", "desired_goal": "[[ 0.48405263 -1.0275805   0.6633325 ]\n [ 1.2858841  -0.33548224 -1.3393227 ]\n [-1.5286498  -1.0902944   0.03032325]\n [-0.17934647  1.1123023   0.5681949 ]]", "observation": "[[ 0.19383524  0.00195633  0.42790607  0.43052822 -0.00497465  0.3592435 ]\n [ 0.19383524  0.00195633  0.42790607  0.43052822 -0.00497465  0.3592435 ]\n [-0.11639074 -0.34614852 -0.13272111 -1.8082856  -1.6978616  -1.3849537 ]\n [-0.5083086   0.33259195  0.33892584 -0.7858022   1.6569867   0.8502351 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.04863565  0.01680663  0.00884386]\n [-0.12578866  0.02681088  0.03398855]\n [-0.05419728 -0.08474252  0.0188862 ]\n [-0.09905962  0.09411886  0.2421644 ]]", "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": 25000, "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": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.2586860232055187, "std_reward": 0.13351411531228163, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-08T12:06:21.996641"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af360c2938bb677fbdfae514891dd1adcb5335fee3ed6dbb5d7f14d46c595ef4
+size 2623