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: -5.71 +/- 0.78
+      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 sha256:58e6d7d60a3155e1ca60fe6b8062eed63fa2539ff1028f483b819a7ee6095653
+size 107924

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 0x0000017FF124EC10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x0000017FF1251200>"
+    },
+    "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": 1690368209926793200,
+    "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": "[[ 0.3228315  -0.02915874  0.496559  ]\n [ 0.3228315  -0.02915874  0.496559  ]\n [ 0.3228315  -0.02915874  0.496559  ]\n [ 0.3228315  -0.02915874  0.496559  ]]",
+        "desired_goal": "[[ 1.2186621   0.58967096 -1.6398163 ]\n [-0.9297532   1.4630755  -0.7145447 ]\n [-1.191079   -1.1240407   0.13982053]\n [ 0.4652091  -1.5084348   1.1327366 ]]",
+        "observation": "[[ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]\n [ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]\n [ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]\n [ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]]"
+    },
+    "_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": "[[-0.11771132  0.0305669   0.23545292]\n [ 0.1327192   0.04787118  0.09412799]\n [ 0.14435753 -0.07207627  0.26230043]\n [ 0.07124031 -0.03215233  0.04692702]]",
+        "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIfEW3XtMDEcCUhpRSlIwBbJRLMowBdJRHQKUcwAz544Z1fZQoaAZoCWgPQwhckC3L16UXwJSGlFKUaBVLMmgWR0ClHIzCcf/4dX2UKGgGaAloD0MIPUZ55uUQHMCUhpRSlGgVSzJoFkdApRxZqO938nV9lChoBmgJaA9DCOMXXknyPBDAlIaUUpRoFUsyaBZHQKUcJWkrPMV1fZQoaAZoCWgPQwjHSsyzkrYiwJSGlFKUaBVLMmgWR0ClHaiQLeANdX2UKGgGaAloD0MI3zR9dsCFEMCUhpRSlGgVSzJoFkdApR11Riw0O3V9lChoBmgJaA9DCBObj2tD5RbAlIaUUpRoFUsyaBZHQKUdQapgkTp1fZQoaAZoCWgPQwjABG7dzVMcwJSGlFKUaBVLMmgWR0ClHQ1pj+aSdX2UKGgGaAloD0MIAfinVIliH8CUhpRSlGgVSzJoFkdApR5zSw4bTHV9lChoBmgJaA9DCDZXzXNEHhbAlIaUUpRoFUsyaBZHQKUeQIOYplV1fZQoaAZoCWgPQwjk+KHSiGkUwJSGlFKUaBVLMmgWR0ClHgznA6+4dX2UKGgGaAloD0MI/Uy9bhHYIcCUhpRSlGgVSzJoFkdApR3ZKnNxEXV9lChoBmgJaA9DCMiakUHushfAlIaUUpRoFUsyaBZHQKUfV6guh9N1fZQoaAZoCWgPQwh87ZklAfofwJSGlFKUaBVLMmgWR0ClHyTg2qDLdX2UKGgGaAloD0MIB7e1hed1GsCUhpRSlGgVSzJoFkdApR7xREWqLnV9lChoBmgJaA9DCMS12sNe4CHAlIaUUpRoFUsyaBZHQKUevYdyT6l1fZQoaAZoCWgPQwhVoYFYNjMYwJSGlFKUaBVLMmgWR0ClIF0dJaq0dX2UKGgGaAloD0MILUFGQIUDF8CUhpRSlGgVSzJoFkdApSAp0p3HJnV9lChoBmgJaA9DCLYODvYm1hfAlIaUUpRoFUsyaBZHQKUf9rkbPyF1fZQoaAZoCWgPQwgC2IAIcYURwJSGlFKUaBVLMmgWR0ClH8L8aXKKdX2UKGgGaAloD0MIT3eeeM42GcCUhpRSlGgVSzJoFkdApSFGEug6EXV9lChoBmgJaA9DCO8dNSbEbB3AlIaUUpRoFUsyaBZHQKUhEsgdOqN1fZQoaAZoCWgPQwhEatrFNIMVwJSGlFKUaBVLMmgWR0ClIN+ueSSvdX2UKGgGaAloD0MIXi7iOzFLFsCUhpRSlGgVSzJoFkdApSCr8k2P1nV9lChoBmgJaA9DCOJ30y071BfAlIaUUpRoFUsyaBZHQKUiIHmig011fZQoaAZoCWgPQwhjuaXVkDgVwJSGlFKUaBVLMmgWR0ClIe0vGp++dX2UKGgGaAloD0MIrHKh8q+lHMCUhpRSlGgVSzJoFkdApSG6FbmlqXV9lChoBmgJaA9DCJIFTODWbRjAlIaUUpRoFUsyaBZHQKUhhdX1ant1fZQoaAZoCWgPQwgoLVxWYVMZwJSGlFKUaBVLMmgWR0ClIxrX+VC5dX2UKGgGaAloD0MIhQfNrnu7EcCUhpRSlGgVSzJoFkdApSLoEKVpsXV9lChoBmgJaA9DCB6NQ/0ujAnAlIaUUpRoFUsyaBZHQKUitHQQcxV1fZQoaAZoCWgPQwi3t1uSA7YYwJSGlFKUaBVLMmgWR0ClIoC2tuDSdX2UKGgGaAloD0MI8WQ3M/rBGMCUhpRSlGgVSzJoFkdApSP44S6DoXV9lChoBmgJaA9DCM0Bgjl6jBzAlIaUUpRoFUsyaBZHQKUjxZamoBJ1fZQoaAZoCWgPQwjEeqNWmG4UwJSGlFKUaBVLMmgWR0ClI5H6MzdldX2UKGgGaAloD0MI0XmNXaJqG8CUhpRSlGgVSzJoFkdApSNdum78N3V9lChoBmgJaA9DCKLvbmWJbhPAlIaUUpRoFUsyaBZHQKUk9Z13dKx1fZQoaAZoCWgPQwhbsb/snlwUwJSGlFKUaBVLMmgWR0ClJMJTER8MdX2UKGgGaAloD0MItcAeEyntFMCUhpRSlGgVSzJoFkdApSSPOObRW3V9lChoBmgJaA9DCCOFsvD1FRjAlIaUUpRoFUsyaBZHQKUkWvlEJBx1fZQoaAZoCWgPQwjumLoru5AfwJSGlFKUaBVLMmgWR0ClJduA7PpqdX2UKGgGaAloD0MIkluTbkskHMCUhpRSlGgVSzJoFkdApSWoNmUW23V9lChoBmgJaA9DCNWuCWmNQRfAlIaUUpRoFUsyaBZHQKUldJlJ6IF1fZQoaAZoCWgPQwiYhXZOsyANwJSGlFKUaBVLMmgWR0ClJUDcuanadX2UKGgGaAloD0MIdjOjHw0XG8CUhpRSlGgVSzJoFkdApSbjLyMDOnV9lChoBmgJaA9DCGpMiLmkagbAlIaUUpRoFUsyaBZHQKUmsGetjkN1fZQoaAZoCWgPQwgyAFRx4yYYwJSGlFKUaBVLMmgWR0ClJn1NpM6BdX2UKGgGaAloD0MIX5fhP90gGMCUhpRSlGgVSzJoFkdApSZJkVeruXV9lChoBmgJaA9DCEq1T8dj5hLAlIaUUpRoFUsyaBZHQKUn35ooNNJ1fZQoaAZoCWgPQwifW+hKBPocwJSGlFKUaBVLMmgWR0ClJ6xO1v2odX2UKGgGaAloD0MIZfz7jAtfIMCUhpRSlGgVSzJoFkdApSd5NRFZxXV9lChoBmgJaA9DCFBtcCL6RRzAlIaUUpRoFUsyaBZHQKUnRXjlxOt1fZQoaAZoCWgPQwj3WtB7Y9gWwJSGlFKUaBVLMmgWR0ClKMseOn2qdX2UKGgGaAloD0MIEY/Ey9N5FMCUhpRSlGgVSzJoFkdApSiYVuaWonV9lChoBmgJaA9DCIl+bf30ryDAlIaUUpRoFUsyaBZHQKUoZT1kDp11fZQoaAZoCWgPQwg4TDRIwZMQwJSGlFKUaBVLMmgWR0ClKDIDxLCfdX2UKGgGaAloD0MIkfP+P05YCsCUhpRSlGgVSzJoFkdApSnVhLGrCHV9lChoBmgJaA9DCPZ5jPLMmyDAlIaUUpRoFUsyaBZHQKUpor2g3991fZQoaAZoCWgPQwhb64uEtgwWwJSGlFKUaBVLMmgWR0ClKW8hLXcydX2UKGgGaAloD0MIXRd+cD7lE8CUhpRSlGgVSzJoFkdApSk64UeuFHV9lChoBmgJaA9DCHe/CvDdphzAlIaUUpRoFUsyaBZHQKUqzq/ub7V1fZQoaAZoCWgPQwjXag97oQARwJSGlFKUaBVLMmgWR0ClKpvoV2zOdX2UKGgGaAloD0MIgzP4+8X8GsCUhpRSlGgVSzJoFkdApSpoS+QEIXV9lChoBmgJaA9DCHkDzHwHzxbAlIaUUpRoFUsyaBZHQKUqNAv+OwR1fZQoaAZoCWgPQwgNAFXcuAUhwJSGlFKUaBVLMmgWR0ClK+Mq8UVSdX2UKGgGaAloD0MIJQaBlUMbG8CUhpRSlGgVSzJoFkdApSuwYtQKr3V9lChoBmgJaA9DCIuMDkjC3hTAlIaUUpRoFUsyaBZHQKUrfUnXumd1fZQoaAZoCWgPQwj60XDK3NwfwJSGlFKUaBVLMmgWR0ClK0mNR3vAdX2UKGgGaAloD0MIr83GSsyTFcCUhpRSlGgVSzJoFkdApSzBMHryD3V9lChoBmgJaA9DCEj5SbVPRxHAlIaUUpRoFUsyaBZHQKUsjeXzDoB1fZQoaAZoCWgPQwgrpWd6iTETwJSGlFKUaBVLMmgWR0ClLFpJf6XTdX2UKGgGaAloD0MIaR7AIr/uE8CUhpRSlGgVSzJoFkdApSwmCbtqpXV9lChoBmgJaA9DCDnRrkLKTxLAlIaUUpRoFUsyaBZHQKUtlgNwzch1fZQoaAZoCWgPQwh3nQ35Z3YWwJSGlFKUaBVLMmgWR0ClLWK46Oo6dX2UKGgGaAloD0MIniYz3lZaF8CUhpRSlGgVSzJoFkdApS0vn2ZiNXV9lChoBmgJaA9DCOqVsgxxDBzAlIaUUpRoFUsyaBZHQKUs+1+iJwd1fZQoaAZoCWgPQwipvYi2Y6oNwJSGlFKUaBVLMmgWR0ClLnjT8YQ8dX2UKGgGaAloD0MIXi7iOzHrFcCUhpRSlGgVSzJoFkdApS5FihFmWnV9lChoBmgJaA9DCD1/2qhO1xLAlIaUUpRoFUsyaBZHQKUuEm/nGKh1fZQoaAZoCWgPQwjeV+VC5V8QwJSGlFKUaBVLMmgWR0ClLd4wqRU4dX2UKGgGaAloD0MIKZZbWg2JD8CUhpRSlGgVSzJoFkdApS9ocghbGHV9lChoBmgJaA9DCIrHRbWI6AvAlIaUUpRoFUsyaBZHQKUvNSeiBXl1fZQoaAZoCWgPQwghPrDjv9AWwJSGlFKUaBVLMmgWR0ClLwIOH310dX2UKGgGaAloD0MImUo/4ey2EMCUhpRSlGgVSzJoFkdApS7NzhgmZ3V9lChoBmgJaA9DCEqyDkdXGRfAlIaUUpRoFUsyaBZHQKUwPswco6V1fZQoaAZoCWgPQwgxeQPMfAcVwJSGlFKUaBVLMmgWR0ClMAuBlMAWdX2UKGgGaAloD0MIBHRfzmy3E8CUhpRSlGgVSzJoFkdApS/X5P/JeXV9lChoBmgJaA9DCFlqvd9oxxbAlIaUUpRoFUsyaBZHQKUvpChN/ON1fZQoaAZoCWgPQwgMBAEydEwawJSGlFKUaBVLMmgWR0ClMQmNR3vAdX2UKGgGaAloD0MI7+Nojqw8GcCUhpRSlGgVSzJoFkdApTDWSB9TgnV9lChoBmgJaA9DCFH1K50PnxjAlIaUUpRoFUsyaBZHQKUwoqvNeMR1fZQoaAZoCWgPQwjnN0w0SFETwJSGlFKUaBVLMmgWR0ClMG5l4C6pdX2UKGgGaAloD0MIkNlZ9E41FsCUhpRSlGgVSzJoFkdApTHhnlGPP3V9lChoBmgJaA9DCBrBxvXvWh7AlIaUUpRoFUsyaBZHQKUxrtWuHN51fZQoaAZoCWgPQwgS+S6lLtkNwJSGlFKUaBVLMmgWR0ClMXw+2VmjdX2UKGgGaAloD0MILdLEO8BTF8CUhpRSlGgVSzJoFkdApTFH/vOQhnV9lChoBmgJaA9DCH7IW65+/B7AlIaUUpRoFUsyaBZHQKUzDtNzr/t1fZQoaAZoCWgPQwhupGyRtDsTwJSGlFKUaBVLMmgWR0ClMtwMYuTSdX2UKGgGaAloD0MIRRK9jGKpFcCUhpRSlGgVSzJoFkdApTKpddE9dXV9lChoBmgJaA9DCJ2DZ0KT5AzAlIaUUpRoFUsyaBZHQKUydTYNAkd1ZS4="
+    },
+    "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 sha256:6db4a119c239fa56ce7969b9984e70701c3efa805b5c2f3eb2e59afb3d40a4e0
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d05736fc46b2b4da86807322e32ad37edd5652a5681c2dcbfc0665f8fa1616c8
+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: Windows-10-10.0.19045-SP0 10.0.19045
+- Python: 3.9.13
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu117
+- GPU Enabled: True
+- Numpy: 1.21.2
+- 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 0x0000017FF124EC10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x0000017FF1251200>"}, "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": 1690368209926793200, "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": "[[ 0.3228315  -0.02915874  0.496559  ]\n [ 0.3228315  -0.02915874  0.496559  ]\n [ 0.3228315  -0.02915874  0.496559  ]\n [ 0.3228315  -0.02915874  0.496559  ]]", "desired_goal": "[[ 1.2186621   0.58967096 -1.6398163 ]\n [-0.9297532   1.4630755  -0.7145447 ]\n [-1.191079   -1.1240407   0.13982053]\n [ 0.4652091  -1.5084348   1.1327366 ]]", "observation": "[[ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]\n [ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]\n [ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]\n [ 0.3228315  -0.02915874  0.496559    0.01621956 -0.00404208  0.01379221]]"}, "_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": "[[-0.11771132  0.0305669   0.23545292]\n [ 0.1327192   0.04787118  0.09412799]\n [ 0.14435753 -0.07207627  0.26230043]\n [ 0.07124031 -0.03215233  0.04692702]]", "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIfEW3XtMDEcCUhpRSlIwBbJRLMowBdJRHQKUcwAz544Z1fZQoaAZoCWgPQwhckC3L16UXwJSGlFKUaBVLMmgWR0ClHIzCcf/4dX2UKGgGaAloD0MIPUZ55uUQHMCUhpRSlGgVSzJoFkdApRxZqO938nV9lChoBmgJaA9DCOMXXknyPBDAlIaUUpRoFUsyaBZHQKUcJWkrPMV1fZQoaAZoCWgPQwjHSsyzkrYiwJSGlFKUaBVLMmgWR0ClHaiQLeANdX2UKGgGaAloD0MI3zR9dsCFEMCUhpRSlGgVSzJoFkdApR11Riw0O3V9lChoBmgJaA9DCBObj2tD5RbAlIaUUpRoFUsyaBZHQKUdQapgkTp1fZQoaAZoCWgPQwjABG7dzVMcwJSGlFKUaBVLMmgWR0ClHQ1pj+aSdX2UKGgGaAloD0MIAfinVIliH8CUhpRSlGgVSzJoFkdApR5zSw4bTHV9lChoBmgJaA9DCDZXzXNEHhbAlIaUUpRoFUsyaBZHQKUeQIOYplV1fZQoaAZoCWgPQwjk+KHSiGkUwJSGlFKUaBVLMmgWR0ClHgznA6+4dX2UKGgGaAloD0MI/Uy9bhHYIcCUhpRSlGgVSzJoFkdApR3ZKnNxEXV9lChoBmgJaA9DCMiakUHushfAlIaUUpRoFUsyaBZHQKUfV6guh9N1fZQoaAZoCWgPQwh87ZklAfofwJSGlFKUaBVLMmgWR0ClHyTg2qDLdX2UKGgGaAloD0MIB7e1hed1GsCUhpRSlGgVSzJoFkdApR7xREWqLnV9lChoBmgJaA9DCMS12sNe4CHAlIaUUpRoFUsyaBZHQKUevYdyT6l1fZQoaAZoCWgPQwhVoYFYNjMYwJSGlFKUaBVLMmgWR0ClIF0dJaq0dX2UKGgGaAloD0MILUFGQIUDF8CUhpRSlGgVSzJoFkdApSAp0p3HJnV9lChoBmgJaA9DCLYODvYm1hfAlIaUUpRoFUsyaBZHQKUf9rkbPyF1fZQoaAZoCWgPQwgC2IAIcYURwJSGlFKUaBVLMmgWR0ClH8L8aXKKdX2UKGgGaAloD0MIT3eeeM42GcCUhpRSlGgVSzJoFkdApSFGEug6EXV9lChoBmgJaA9DCO8dNSbEbB3AlIaUUpRoFUsyaBZHQKUhEsgdOqN1fZQoaAZoCWgPQwhEatrFNIMVwJSGlFKUaBVLMmgWR0ClIN+ueSSvdX2UKGgGaAloD0MIXi7iOzFLFsCUhpRSlGgVSzJoFkdApSCr8k2P1nV9lChoBmgJaA9DCOJ30y071BfAlIaUUpRoFUsyaBZHQKUiIHmig011fZQoaAZoCWgPQwhjuaXVkDgVwJSGlFKUaBVLMmgWR0ClIe0vGp++dX2UKGgGaAloD0MIrHKh8q+lHMCUhpRSlGgVSzJoFkdApSG6FbmlqXV9lChoBmgJaA9DCJIFTODWbRjAlIaUUpRoFUsyaBZHQKUhhdX1ant1fZQoaAZoCWgPQwgoLVxWYVMZwJSGlFKUaBVLMmgWR0ClIxrX+VC5dX2UKGgGaAloD0MIhQfNrnu7EcCUhpRSlGgVSzJoFkdApSLoEKVpsXV9lChoBmgJaA9DCB6NQ/0ujAnAlIaUUpRoFUsyaBZHQKUitHQQcxV1fZQoaAZoCWgPQwi3t1uSA7YYwJSGlFKUaBVLMmgWR0ClIoC2tuDSdX2UKGgGaAloD0MI8WQ3M/rBGMCUhpRSlGgVSzJoFkdApSP44S6DoXV9lChoBmgJaA9DCM0Bgjl6jBzAlIaUUpRoFUsyaBZHQKUjxZamoBJ1fZQoaAZoCWgPQwjEeqNWmG4UwJSGlFKUaBVLMmgWR0ClI5H6MzdldX2UKGgGaAloD0MI0XmNXaJqG8CUhpRSlGgVSzJoFkdApSNdum78N3V9lChoBmgJaA9DCKLvbmWJbhPAlIaUUpRoFUsyaBZHQKUk9Z13dKx1fZQoaAZoCWgPQwhbsb/snlwUwJSGlFKUaBVLMmgWR0ClJMJTER8MdX2UKGgGaAloD0MItcAeEyntFMCUhpRSlGgVSzJoFkdApSSPOObRW3V9lChoBmgJaA9DCCOFsvD1FRjAlIaUUpRoFUsyaBZHQKUkWvlEJBx1fZQoaAZoCWgPQwjumLoru5AfwJSGlFKUaBVLMmgWR0ClJduA7PpqdX2UKGgGaAloD0MIkluTbkskHMCUhpRSlGgVSzJoFkdApSWoNmUW23V9lChoBmgJaA9DCNWuCWmNQRfAlIaUUpRoFUsyaBZHQKUldJlJ6IF1fZQoaAZoCWgPQwiYhXZOsyANwJSGlFKUaBVLMmgWR0ClJUDcuanadX2UKGgGaAloD0MIdjOjHw0XG8CUhpRSlGgVSzJoFkdApSbjLyMDOnV9lChoBmgJaA9DCGpMiLmkagbAlIaUUpRoFUsyaBZHQKUmsGetjkN1fZQoaAZoCWgPQwgyAFRx4yYYwJSGlFKUaBVLMmgWR0ClJn1NpM6BdX2UKGgGaAloD0MIX5fhP90gGMCUhpRSlGgVSzJoFkdApSZJkVeruXV9lChoBmgJaA9DCEq1T8dj5hLAlIaUUpRoFUsyaBZHQKUn35ooNNJ1fZQoaAZoCWgPQwifW+hKBPocwJSGlFKUaBVLMmgWR0ClJ6xO1v2odX2UKGgGaAloD0MIZfz7jAtfIMCUhpRSlGgVSzJoFkdApSd5NRFZxXV9lChoBmgJaA9DCFBtcCL6RRzAlIaUUpRoFUsyaBZHQKUnRXjlxOt1fZQoaAZoCWgPQwj3WtB7Y9gWwJSGlFKUaBVLMmgWR0ClKMseOn2qdX2UKGgGaAloD0MIEY/Ey9N5FMCUhpRSlGgVSzJoFkdApSiYVuaWonV9lChoBmgJaA9DCIl+bf30ryDAlIaUUpRoFUsyaBZHQKUoZT1kDp11fZQoaAZoCWgPQwg4TDRIwZMQwJSGlFKUaBVLMmgWR0ClKDIDxLCfdX2UKGgGaAloD0MIkfP+P05YCsCUhpRSlGgVSzJoFkdApSnVhLGrCHV9lChoBmgJaA9DCPZ5jPLMmyDAlIaUUpRoFUsyaBZHQKUpor2g3991fZQoaAZoCWgPQwhb64uEtgwWwJSGlFKUaBVLMmgWR0ClKW8hLXcydX2UKGgGaAloD0MIXRd+cD7lE8CUhpRSlGgVSzJoFkdApSk64UeuFHV9lChoBmgJaA9DCHe/CvDdphzAlIaUUpRoFUsyaBZHQKUqzq/ub7V1fZQoaAZoCWgPQwjXag97oQARwJSGlFKUaBVLMmgWR0ClKpvoV2zOdX2UKGgGaAloD0MIgzP4+8X8GsCUhpRSlGgVSzJoFkdApSpoS+QEIXV9lChoBmgJaA9DCHkDzHwHzxbAlIaUUpRoFUsyaBZHQKUqNAv+OwR1fZQoaAZoCWgPQwgNAFXcuAUhwJSGlFKUaBVLMmgWR0ClK+Mq8UVSdX2UKGgGaAloD0MIJQaBlUMbG8CUhpRSlGgVSzJoFkdApSuwYtQKr3V9lChoBmgJaA9DCIuMDkjC3hTAlIaUUpRoFUsyaBZHQKUrfUnXumd1fZQoaAZoCWgPQwj60XDK3NwfwJSGlFKUaBVLMmgWR0ClK0mNR3vAdX2UKGgGaAloD0MIr83GSsyTFcCUhpRSlGgVSzJoFkdApSzBMHryD3V9lChoBmgJaA9DCEj5SbVPRxHAlIaUUpRoFUsyaBZHQKUsjeXzDoB1fZQoaAZoCWgPQwgrpWd6iTETwJSGlFKUaBVLMmgWR0ClLFpJf6XTdX2UKGgGaAloD0MIaR7AIr/uE8CUhpRSlGgVSzJoFkdApSwmCbtqpXV9lChoBmgJaA9DCDnRrkLKTxLAlIaUUpRoFUsyaBZHQKUtlgNwzch1fZQoaAZoCWgPQwh3nQ35Z3YWwJSGlFKUaBVLMmgWR0ClLWK46Oo6dX2UKGgGaAloD0MIniYz3lZaF8CUhpRSlGgVSzJoFkdApS0vn2ZiNXV9lChoBmgJaA9DCOqVsgxxDBzAlIaUUpRoFUsyaBZHQKUs+1+iJwd1fZQoaAZoCWgPQwipvYi2Y6oNwJSGlFKUaBVLMmgWR0ClLnjT8YQ8dX2UKGgGaAloD0MIXi7iOzHrFcCUhpRSlGgVSzJoFkdApS5FihFmWnV9lChoBmgJaA9DCD1/2qhO1xLAlIaUUpRoFUsyaBZHQKUuEm/nGKh1fZQoaAZoCWgPQwjeV+VC5V8QwJSGlFKUaBVLMmgWR0ClLd4wqRU4dX2UKGgGaAloD0MIKZZbWg2JD8CUhpRSlGgVSzJoFkdApS9ocghbGHV9lChoBmgJaA9DCIrHRbWI6AvAlIaUUpRoFUsyaBZHQKUvNSeiBXl1fZQoaAZoCWgPQwghPrDjv9AWwJSGlFKUaBVLMmgWR0ClLwIOH310dX2UKGgGaAloD0MImUo/4ey2EMCUhpRSlGgVSzJoFkdApS7NzhgmZ3V9lChoBmgJaA9DCEqyDkdXGRfAlIaUUpRoFUsyaBZHQKUwPswco6V1fZQoaAZoCWgPQwgxeQPMfAcVwJSGlFKUaBVLMmgWR0ClMAuBlMAWdX2UKGgGaAloD0MIBHRfzmy3E8CUhpRSlGgVSzJoFkdApS/X5P/JeXV9lChoBmgJaA9DCFlqvd9oxxbAlIaUUpRoFUsyaBZHQKUvpChN/ON1fZQoaAZoCWgPQwgMBAEydEwawJSGlFKUaBVLMmgWR0ClMQmNR3vAdX2UKGgGaAloD0MI7+Nojqw8GcCUhpRSlGgVSzJoFkdApTDWSB9TgnV9lChoBmgJaA9DCFH1K50PnxjAlIaUUpRoFUsyaBZHQKUwoqvNeMR1fZQoaAZoCWgPQwjnN0w0SFETwJSGlFKUaBVLMmgWR0ClMG5l4C6pdX2UKGgGaAloD0MIkNlZ9E41FsCUhpRSlGgVSzJoFkdApTHhnlGPP3V9lChoBmgJaA9DCBrBxvXvWh7AlIaUUpRoFUsyaBZHQKUxrtWuHN51fZQoaAZoCWgPQwgS+S6lLtkNwJSGlFKUaBVLMmgWR0ClMXw+2VmjdX2UKGgGaAloD0MILdLEO8BTF8CUhpRSlGgVSzJoFkdApTFH/vOQhnV9lChoBmgJaA9DCH7IW65+/B7AlIaUUpRoFUsyaBZHQKUzDtNzr/t1fZQoaAZoCWgPQwhupGyRtDsTwJSGlFKUaBVLMmgWR0ClMtwMYuTSdX2UKGgGaAloD0MIRRK9jGKpFcCUhpRSlGgVSzJoFkdApTKpddE9dXV9lChoBmgJaA9DCJ2DZ0KT5AzAlIaUUpRoFUsyaBZHQKUydTYNAkd1ZS4="}, "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": "Windows-10-10.0.19045-SP0 10.0.19045", "Python": "3.9.13", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu117", "GPU Enabled": "True", "Numpy": "1.21.2", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -5.714640921913087, "std_reward": 0.7835631042349195, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-26T16:55:00.676960"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:609290a3992274f44eb727b22ac0f0e84e2137d0212b9bad645443f3f8f34235
+size 2464