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: -2.51 +/- 0.70
+      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:ae5fd5ae91a2709cccf1e9ce2e91fff24a2f227ae3f57d8aa587c0233cbaddde
+size 108011

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 0x7f9a47ecf940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f9a47ed20f0>"
+    },
+    "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": 1676559889125843346,
+    "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": "[[0.4247919  0.00100523 0.5265598 ]\n [0.4247919  0.00100523 0.5265598 ]\n [0.4247919  0.00100523 0.5265598 ]\n [0.4247919  0.00100523 0.5265598 ]]",
+        "desired_goal": "[[ 0.43834624  0.01162337  0.38017958]\n [ 0.0059276  -0.54679364 -0.23286323]\n [ 0.15678908 -1.6702801   0.5933105 ]\n [ 1.1216453  -0.57488656 -0.17487454]]",
+        "observation": "[[ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]\n [ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]\n [ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]\n [ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]]"
+    },
+    "_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": "[[ 0.1233915  -0.03305786  0.17847005]\n [ 0.02730498  0.13656656  0.26069713]\n [ 0.07376274  0.06685727  0.24897914]\n [-0.05040588 -0.03128771  0.09436223]]",
+        "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIMbH5uDaU+L+UhpRSlIwBbJRLMowBdJRHQKcEIhHskY51fZQoaAZoCWgPQwhzZVBtcCIEwJSGlFKUaBVLMmgWR0CnA8pCSidrdX2UKGgGaAloD0MIgZVDi2wHAMCUhpRSlGgVSzJoFkdApwN001qFiHV9lChoBmgJaA9DCICAtWrXBPW/lIaUUpRoFUsyaBZHQKcDID4gzP91fZQoaAZoCWgPQwjJIHcRpngQwJSGlFKUaBVLMmgWR0CnBQs3yZrpdX2UKGgGaAloD0MIDXBBtix/AsCUhpRSlGgVSzJoFkdApwSzGxUvPHV9lChoBmgJaA9DCMVVZd8VYQ7AlIaUUpRoFUsyaBZHQKcEXbFCLMt1fZQoaAZoCWgPQwgfhlYnZygBwJSGlFKUaBVLMmgWR0CnBAljmSyMdX2UKGgGaAloD0MI3nL1Y5O8A8CUhpRSlGgVSzJoFkdApwYBFb3XZ3V9lChoBmgJaA9DCFjnGJC93vu/lIaUUpRoFUsyaBZHQKcFqVZcLSh1fZQoaAZoCWgPQwgqqn6l88EPwJSGlFKUaBVLMmgWR0CnBVRISUTtdX2UKGgGaAloD0MI0R+aeXKN87+UhpRSlGgVSzJoFkdApwT/7Lt/nXV9lChoBmgJaA9DCN/8hokGCQjAlIaUUpRoFUsyaBZHQKcHGQ2/BWR1fZQoaAZoCWgPQwg51sVtNADyv5SGlFKUaBVLMmgWR0CnBsIJzDGcdX2UKGgGaAloD0MIZtmTwOac5L+UhpRSlGgVSzJoFkdApwZtR77bc3V9lChoBmgJaA9DCA3FHW/yOwzAlIaUUpRoFUsyaBZHQKcGGXxe9jB1fZQoaAZoCWgPQwjzPSMRGsHsv5SGlFKUaBVLMmgWR0CnCMyEDhcadX2UKGgGaAloD0MIj/0sliJ5CsCUhpRSlGgVSzJoFkdApwh1X3g1nHV9lChoBmgJaA9DCD7NyYtMQA3AlIaUUpRoFUsyaBZHQKcIIL7XQMR1fZQoaAZoCWgPQwiMoZxoV6Hjv5SGlFKUaBVLMmgWR0CnB80oa1kUdX2UKGgGaAloD0MIUFCKVu6F87+UhpRSlGgVSzJoFkdApwprDVH4GnV9lChoBmgJaA9DCE/LD1zlifS/lIaUUpRoFUsyaBZHQKcKE+dsi0R1fZQoaAZoCWgPQwifHXBdMeP4v5SGlFKUaBVLMmgWR0CnCb76P8yfdX2UKGgGaAloD0MIvFtZorMcEsCUhpRSlGgVSzJoFkdApwlrU7Sy+3V9lChoBmgJaA9DCAGFevoIvAHAlIaUUpRoFUsyaBZHQKcMC9h7Vrh1fZQoaAZoCWgPQwh9PsqIC8AJwJSGlFKUaBVLMmgWR0CnC7TbeuV5dX2UKGgGaAloD0MIqVDdXPwND8CUhpRSlGgVSzJoFkdApwtgs052hnV9lChoBmgJaA9DCJUnEHaKVee/lIaUUpRoFUsyaBZHQKcLDQC0WuZ1fZQoaAZoCWgPQwj/zvboDXfkv5SGlFKUaBVLMmgWR0CnDZzhgmZ3dX2UKGgGaAloD0MITI3Qz9QrAMCUhpRSlGgVSzJoFkdApw1FdgOSXHV9lChoBmgJaA9DCJfK2xFOi/e/lIaUUpRoFUsyaBZHQKcM8KfFrEd1fZQoaAZoCWgPQwhtHofB/JUJwJSGlFKUaBVLMmgWR0CnDJ1g6U7kdX2UKGgGaAloD0MIAcKHEi0ZDMCUhpRSlGgVSzJoFkdApw83YDklu3V9lChoBmgJaA9DCC6QoPgxRg/AlIaUUpRoFUsyaBZHQKcO4G0NSZV1fZQoaAZoCWgPQwiBXyNJEC7rv5SGlFKUaBVLMmgWR0CnDowdKdxydX2UKGgGaAloD0MIlPWbienC/b+UhpRSlGgVSzJoFkdApw44QWepXXV9lChoBmgJaA9DCJnTZTGxuf+/lIaUUpRoFUsyaBZHQKcQ4wN9YwJ1fZQoaAZoCWgPQwiGWtO843QCwJSGlFKUaBVLMmgWR0CnEIwuEmICdX2UKGgGaAloD0MIGapiKv1E9r+UhpRSlGgVSzJoFkdApxA3gzguRXV9lChoBmgJaA9DCEbu6eqOhfu/lIaUUpRoFUsyaBZHQKcP5BLwnYx1fZQoaAZoCWgPQwi6oSk7/WAMwJSGlFKUaBVLMmgWR0CnEfgmZ3LWdX2UKGgGaAloD0MIKnCyDdxB/7+UhpRSlGgVSzJoFkdApxGgDV6NVHV9lChoBmgJaA9DCCFzZVBtEAPAlIaUUpRoFUsyaBZHQKcRSqXnhbZ1fZQoaAZoCWgPQwgm/b0UHvT0v5SGlFKUaBVLMmgWR0CnEPYKx9ofdX2UKGgGaAloD0MIFXE6yVYX+r+UhpRSlGgVSzJoFkdApxMCD28IzHV9lChoBmgJaA9DCJJZvcPtEPu/lIaUUpRoFUsyaBZHQKcSqj8k2P11fZQoaAZoCWgPQwiPN/ktOgkQwJSGlFKUaBVLMmgWR0CnElUhFEy+dX2UKGgGaAloD0MIGtzWFp5X9b+UhpRSlGgVSzJoFkdApxIAuK4x13V9lChoBmgJaA9DCG2tLxLasgjAlIaUUpRoFUsyaBZHQKcT//rB0p51fZQoaAZoCWgPQwiDUrRyLzAMwJSGlFKUaBVLMmgWR0CnE6fwZwXJdX2UKGgGaAloD0MIK/wZ3qzB9L+UhpRSlGgVSzJoFkdApxNS3solU3V9lChoBmgJaA9DCBu4A3XKI+q/lIaUUpRoFUsyaBZHQKcS/o+wC8x1fZQoaAZoCWgPQwh/9bhvtc78v5SGlFKUaBVLMmgWR0CnFOIEr5IpdX2UKGgGaAloD0MIrrg4KjeRAcCUhpRSlGgVSzJoFkdApxSJ7TlT33V9lChoBmgJaA9DCCh/944aQxHAlIaUUpRoFUsyaBZHQKcUNHcUM5R1fZQoaAZoCWgPQwibVDTW/u4AwJSGlFKUaBVLMmgWR0CnE9/YjB2wdX2UKGgGaAloD0MI6KViY17HCMCUhpRSlGgVSzJoFkdApxXNc8kleHV9lChoBmgJaA9DCPeTMT7MPgXAlIaUUpRoFUsyaBZHQKcVdWAf+0h1fZQoaAZoCWgPQwjk84qnHukIwJSGlFKUaBVLMmgWR0CnFR/0Eov0dX2UKGgGaAloD0MIVkW4yajy87+UhpRSlGgVSzJoFkdApxTLeTFERnV9lChoBmgJaA9DCD4D6s2oOQLAlIaUUpRoFUsyaBZHQKcWw1mapgl1fZQoaAZoCWgPQwg5Qgby7LL3v5SGlFKUaBVLMmgWR0CnFmtb9qDcdX2UKGgGaAloD0MIu0bLgR4qA8CUhpRSlGgVSzJoFkdApxYV+w1R+HV9lChoBmgJaA9DCE8eFmpNM/6/lIaUUpRoFUsyaBZHQKcVwVxjriV1fZQoaAZoCWgPQwh9PV+zXDb6v5SGlFKUaBVLMmgWR0CnF8iQtBfKdX2UKGgGaAloD0MIqruyCwZX5L+UhpRSlGgVSzJoFkdApxdwod+5OXV9lChoBmgJaA9DCDikUYGTjQTAlIaUUpRoFUsyaBZHQKcXGygPEsJ1fZQoaAZoCWgPQwgvFLAdjBgRwJSGlFKUaBVLMmgWR0CnFsbSRbKSdX2UKGgGaAloD0MIru/DQUK0CsCUhpRSlGgVSzJoFkdApxjxFNL13HV9lChoBmgJaA9DCLIRiNf1iwfAlIaUUpRoFUsyaBZHQKcYmUNayKN1fZQoaAZoCWgPQwgbZJKRs1AYwJSGlFKUaBVLMmgWR0CnGETTfBN3dX2UKGgGaAloD0MIFJUNayorBcCUhpRSlGgVSzJoFkdApxfwNEw353V9lChoBmgJaA9DCCzUmuYd5wPAlIaUUpRoFUsyaBZHQKcZ6XHBDXx1fZQoaAZoCWgPQwjJyFnY014SwJSGlFKUaBVLMmgWR0CnGZGShakidX2UKGgGaAloD0MIMJsAw/In+r+UhpRSlGgVSzJoFkdApxk8Z1mrbXV9lChoBmgJaA9DCFK5iVqa+wLAlIaUUpRoFUsyaBZHQKcY5+3H7xd1fZQoaAZoCWgPQwgRHQJHAg35v5SGlFKUaBVLMmgWR0CnGvNE5QxfdX2UKGgGaAloD0MIroIY6Nr3BcCUhpRSlGgVSzJoFkdApxqbfzjFQ3V9lChoBmgJaA9DCADl795RAwPAlIaUUpRoFUsyaBZHQKcaRmEGqxV1fZQoaAZoCWgPQwgkC5jArZsDwJSGlFKUaBVLMmgWR0CnGfIbwSamdX2UKGgGaAloD0MIknh5Ole0CcCUhpRSlGgVSzJoFkdApxv6m2sq8XV9lChoBmgJaA9DCDv8NVmjnvi/lIaUUpRoFUsyaBZHQKcbouez2OB1fZQoaAZoCWgPQwjXFp6Xik0QwJSGlFKUaBVLMmgWR0CnG028qWkadX2UKGgGaAloD0MI8mCL3T4r+7+UhpRSlGgVSzJoFkdApxr5H09QoHV9lChoBmgJaA9DCIQNT6+UBQvAlIaUUpRoFUsyaBZHQKcdDWo3rD91fZQoaAZoCWgPQwhr8SkAxrMSwJSGlFKUaBVLMmgWR0CnHLWhZha1dX2UKGgGaAloD0MIJJwWvOhr+L+UhpRSlGgVSzJoFkdApxxgdZJTVHV9lChoBmgJaA9DCOknnN1apvi/lIaUUpRoFUsyaBZHQKccDCJGe+V1fZQoaAZoCWgPQwiDa+7ofzn4v5SGlFKUaBVLMmgWR0CnHf0kv9LpdX2UKGgGaAloD0MI9tA+VvBbDsCUhpRSlGgVSzJoFkdApx2lTYNAknV9lChoBmgJaA9DCDEjvD0IwfS/lIaUUpRoFUsyaBZHQKcdUCI1tO51fZQoaAZoCWgPQwh7aB8r+A0CwJSGlFKUaBVLMmgWR0CnHPuXNTtLdX2UKGgGaAloD0MIb2dfeZDe8r+UhpRSlGgVSzJoFkdApx71TWGyonV9lChoBmgJaA9DCNTwLawbrwzAlIaUUpRoFUsyaBZHQKcenX2/SIB1fZQoaAZoCWgPQwinQGZn0TsKwJSGlFKUaBVLMmgWR0CnHkgTIvJzdX2UKGgGaAloD0MI3SdHAaIgBsCUhpRSlGgVSzJoFkdApx3zy+YdAHV9lChoBmgJaA9DCP2iBP2FXgPAlIaUUpRoFUsyaBZHQKcgBNSqEOB1fZQoaAZoCWgPQwiFQ2/x8B7tv5SGlFKUaBVLMmgWR0CnH6zp5eJIdX2UKGgGaAloD0MISzygbMqV+b+UhpRSlGgVSzJoFkdApx9XvjOs1nV9lChoBmgJaA9DCEPjiSDOYwLAlIaUUpRoFUsyaBZHQKcfA1kUbkx1ZS4="
+    },
+    "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:d48608398fdfd338a46720d0888336ed2fd6ce54b0c90154d891228afda1e42f
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64c6c05aa83db1baa0e2ad98cd0f19d611d4f55247d45e5da59ec9518f196f35
+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 0x7f9a47ecf940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9a47ed20f0>"}, "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:": "gAWVUgMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZSMAUOUdJRSlIwEaGlnaJRoHSiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBVLA4WUaCB0lFKUjA1ib3VuZGVkX2JlbG93lGgdKJYDAAAAAAAAAAEBAZRoEowCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIHSUUpSMDWJvdW5kZWRfYWJvdmWUaB0olgMAAAAAAAAAAQEBlGgsSwOFlGggdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZRoIHSUUpRoI2gdKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFUsDhZRoIHSUUpRoKGgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoMmgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoN051YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgVaBhLBoWUaBpoHSiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBVLBoWUaCB0lFKUaCNoHSiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBVLBoWUaCB0lFKUaChoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDJoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDdOdWJ1aBhOaBBOaDdOdWIu", "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": 1676559889125843346, "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": "[[0.4247919  0.00100523 0.5265598 ]\n [0.4247919  0.00100523 0.5265598 ]\n [0.4247919  0.00100523 0.5265598 ]\n [0.4247919  0.00100523 0.5265598 ]]", "desired_goal": "[[ 0.43834624  0.01162337  0.38017958]\n [ 0.0059276  -0.54679364 -0.23286323]\n [ 0.15678908 -1.6702801   0.5933105 ]\n [ 1.1216453  -0.57488656 -0.17487454]]", "observation": "[[ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]\n [ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]\n [ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]\n [ 0.4247919   0.00100523  0.5265598  -0.00168414 -0.00201896 -0.0086277 ]]"}, "_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": "[[ 0.1233915  -0.03305786  0.17847005]\n [ 0.02730498  0.13656656  0.26069713]\n [ 0.07376274  0.06685727  0.24897914]\n [-0.05040588 -0.03128771  0.09436223]]", "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": -2.5132307931780815, "std_reward": 0.7015280965172281, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-16T15:54:13.470254"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b47da5ec51161bc3e76261b52c3d26fb0bfe46caf3a56ffa49315bbfeedda943
+size 3056