Initial parapmeters

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: -0.67 +/- 0.32
+      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 sha256:65806ad351136d3ad3af7c7854d68d8b11030871d207eda7a7c393c52816a17b
+size 108023

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7fcd5b943b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fcd5b93cf60>"
+    },
+    "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
+        }
+    },
+    "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:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1674208064086254225,
+    "learning_rate": 0.0007,
+    "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": "[[ 0.40754005 -0.01364603  0.5636976 ]\n [ 0.40754005 -0.01364603  0.5636976 ]\n [ 0.40754005 -0.01364603  0.5636976 ]\n [ 0.40754005 -0.01364603  0.5636976 ]]",
+        "desired_goal": "[[-1.4755331  -0.28382796  1.1902438 ]\n [ 0.01823773 -0.7885203  -0.8831108 ]\n [ 1.2206469   0.6570143   1.2160841 ]\n [ 0.6601476   1.5437133   0.3635448 ]]",
+        "observation": "[[ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]\n [ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]\n [ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]\n [ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]]"
+    },
+    "_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": "[[ 0.04509012  0.00273796  0.0536042 ]\n [ 0.00712994 -0.04278091  0.06735338]\n [-0.06182146 -0.13766585  0.18137391]\n [-0.08847965  0.12048101  0.20541432]]",
+        "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,
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6148245ab69dd1b121ecbc6b84ec244e9f3ef4e18905649e830d5fa466c20fe4
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58919ff1e01796dbff00ed7b9ccdebfc0d2b19d29d93766c952af68730298b6d
+size 46014

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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7fcd5b943b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcd5b93cf60>"}, "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}}, "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, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674208064086254225, "learning_rate": 0.0007, "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": "[[ 0.40754005 -0.01364603  0.5636976 ]\n [ 0.40754005 -0.01364603  0.5636976 ]\n [ 0.40754005 -0.01364603  0.5636976 ]\n [ 0.40754005 -0.01364603  0.5636976 ]]", "desired_goal": "[[-1.4755331  -0.28382796  1.1902438 ]\n [ 0.01823773 -0.7885203  -0.8831108 ]\n [ 1.2206469   0.6570143   1.2160841 ]\n [ 0.6601476   1.5437133   0.3635448 ]]", "observation": "[[ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]\n [ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]\n [ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]\n [ 0.40754005 -0.01364603  0.5636976   0.01246209 -0.00325867  0.01302634]]"}, "_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": "[[ 0.04509012  0.00273796  0.0536042 ]\n [ 0.00712994 -0.04278091  0.06735338]\n [-0.06182146 -0.13766585  0.18137391]\n [-0.08847965  0.12048101  0.20541432]]", "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.6681230585469166, "std_reward": 0.32095504496746496, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-20T10:58:56.859041"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca34bc92561760521af44f7c0eec4d041cc1046c9a5e16f480a63c92612a0472
+size 3212