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: -0.23 +/- 0.11
+      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:d0c469c3ff42db574856b558eeba559d4eb04a8e5ab7186dee4b04618c32dc13
+size 106828

a2c-PandaReachDense-v3/_stable_baselines3_version

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

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 0x7df0d83cf6d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7df0d83d0b00>"
+    },
+    "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": 100000,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691924314419003707,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.17783882  0.02560897  0.43724495]\n [-0.864593    0.78742635  0.5755285 ]\n [ 0.82771987 -0.68923354  0.53880817]\n [ 0.6687064  -0.2014205   0.3297631 ]]",
+        "desired_goal": "[[ 0.5855502   1.0691469   1.5261332 ]\n [-0.8530271   0.7423177   1.5060236 ]\n [ 1.060477   -1.5635779   0.44264764]\n [ 1.3414273  -0.49928057 -0.50351197]]",
+        "observation": "[[ 0.17783882  0.02560897  0.43724495  0.33458847  0.00501916  0.29048193]\n [-0.864593    0.78742635  0.5755285  -1.0389655   1.140589    1.5899885 ]\n [ 0.82771987 -0.68923354  0.53880817  0.14144886 -0.92570484 -0.976267  ]\n [ 0.6687064  -0.2014205   0.3297631   1.447468   -1.6374816  -1.3140768 ]]"
+    },
+    "_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": "[[ 0.02801153 -0.0469976   0.09339532]\n [-0.146405    0.11429998  0.2607298 ]\n [-0.07905737  0.0129309   0.25384834]\n [ 0.03072531 -0.08116937  0.04185049]]",
+        "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": 5000,
+    "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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0eb4e3f225cc9a745ad077e0e88b9c36ddfa1caa7d30a39cd94f2f554ae1d6ae
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47b0cd2b1fda8d4c8968de49f5413dfa636b0916543ed15c1def2b8f94375723
+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: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- 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 0x7df0d83cf6d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7df0d83d0b00>"}, "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": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691924314419003707, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.17783882  0.02560897  0.43724495]\n [-0.864593    0.78742635  0.5755285 ]\n [ 0.82771987 -0.68923354  0.53880817]\n [ 0.6687064  -0.2014205   0.3297631 ]]", "desired_goal": "[[ 0.5855502   1.0691469   1.5261332 ]\n [-0.8530271   0.7423177   1.5060236 ]\n [ 1.060477   -1.5635779   0.44264764]\n [ 1.3414273  -0.49928057 -0.50351197]]", "observation": "[[ 0.17783882  0.02560897  0.43724495  0.33458847  0.00501916  0.29048193]\n [-0.864593    0.78742635  0.5755285  -1.0389655   1.140589    1.5899885 ]\n [ 0.82771987 -0.68923354  0.53880817  0.14144886 -0.92570484 -0.976267  ]\n [ 0.6687064  -0.2014205   0.3297631   1.447468   -1.6374816  -1.3140768 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAbnjlPI+AQL0MRr89MesVvhwW6j1gfoU+1OihvSXcUzxp+IE+pbP7PCE8pr1rays9lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==", "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": "[[ 0.02801153 -0.0469976   0.09339532]\n [-0.146405    0.11429998  0.2607298 ]\n [-0.07905737  0.0129309   0.25384834]\n [ 0.03072531 -0.08116937  0.04185049]]", "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": 5000, "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": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.232224721647799, "std_reward": 0.10821074163948756, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-13T11:20:28.653905"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd7c6ee45763fdb7271a27455fc79539c036a24b4b7a6855c0157150e14a8a7a
+size 2623