Initial commit

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: -10.04 +/- 4.48
+      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 sha256:511055bbbf6e8cffdfe4f284d3eb68cfedd2166f180827ab181e8832a0fe3610
+size 107782

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7d424dcbda20>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d424dcb9780>"
+    },
+    "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": 1689846655169514109,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAGBaWPpQUxDyiLuI+GBaWPpQUxDyiLuI+GBaWPpQUxDyiLuI+GBaWPpQUxDyiLuI+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAxu22PhT2Lr8rERA/3ZdsvlSGxT9nE6U/ya/6vubFRD9UegC/dxGYP4v/hL9jpKS+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAAYFpY+lBTEPKIu4j7WGx481nftOpTLoDoYFpY+lBTEPKIu4j7WGx481nftOpTLoDoYFpY+lBTEPKIu4j7WGx481nftOpTLoDoYFpY+lBTEPKIu4j7WGx481nftOpTLoDqUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[0.2931373  0.02393559 0.44176203]\n [0.2931373  0.02393559 0.44176203]\n [0.2931373  0.02393559 0.44176203]\n [0.2931373  0.02393559 0.44176203]]",
+        "desired_goal": "[[ 0.35728282 -0.68344235  0.56276196]\n [-0.23104806  1.5431619   1.2896546 ]\n [-0.48962238  0.7686447  -0.5018666 ]\n [ 1.188033   -1.0390486  -0.32156667]]",
+        "observation": "[[0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]\n [0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]\n [0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]\n [0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]]"
+    },
+    "_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": "[[-0.1368684  -0.0429192   0.29404974]\n [ 0.05684342 -0.07561287  0.26959425]\n [ 0.11507384 -0.12555481  0.15487893]\n [-0.07302476  0.05863418  0.01543475]]",
+        "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 '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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0711d34236785b53c91d5feaeb6714e564a114b5fda07848df4d6d16006f2d6d
+size 44606

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aaccc83f5fdd0c72a525a7115fa94047a81d1f3efd9d43cdf7f75d44fc74774f
+size 45886

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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: False
+- Numpy: 1.22.4
+- 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 0x7d424dcbda20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d424dcb9780>"}, "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": 1689846655169514109, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.2931373  0.02393559 0.44176203]\n [0.2931373  0.02393559 0.44176203]\n [0.2931373  0.02393559 0.44176203]\n [0.2931373  0.02393559 0.44176203]]", "desired_goal": "[[ 0.35728282 -0.68344235  0.56276196]\n [-0.23104806  1.5431619   1.2896546 ]\n [-0.48962238  0.7686447  -0.5018666 ]\n [ 1.188033   -1.0390486  -0.32156667]]", "observation": "[[0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]\n [0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]\n [0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]\n [0.2931373  0.02393559 0.44176203 0.00965019 0.00181174 0.00122677]]"}, "_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": "[[-0.1368684  -0.0429192   0.29404974]\n [ 0.05684342 -0.07561287  0.26959425]\n [ 0.11507384 -0.12555481  0.15487893]\n [-0.07302476  0.05863418  0.01543475]]", "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 '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, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -10.044971652887762, "std_reward": 4.482348532988952, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-20T10:51:31.594282"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93cc5c4d1b02c0dd7eea2c30a159bf4ac90b3c2fa4802d4b21e678cc0f7f06fa
+size 2387