Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaPushDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaPushDense-v2
+      type: PandaPushDense-v2
+    metrics:
+    - type: mean_reward
+      value: -2.50 +/- 1.79
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaPushDense-v2**
+This is a trained model of a **A2C** agent playing **PandaPushDense-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-PandaPushDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e3b1b6210f5fbe63aedf0a1f70e83a52022d94ebe8b696a09457eb2fe9eac0d
+size 107992

a2c-PandaPushDense-v2/_stable_baselines3_version

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

a2c-PandaPushDense-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 0x7f9dbeae5790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f9dbeae3fc0>"
+    },
+    "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": 1500000,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678785114775136859,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAStriPksuFj1TRRU/StriPksuFj1TRRU/StriPksuFj1TRRU/StriPksuFj1TRRU/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAyXpWvjVhx78Xv+q+qKKyvy/ya79G7rm/ThEXPqr/0T4X07I/HaGKP+bdSD/7sxG+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAABK2uI+Sy4WPVNFFT9KP4A8MsxrO2ksDzxK2uI+Sy4WPVNFFT9KP4A8MsxrO2ksDzxK2uI+Sy4WPVNFFT9KP4A8MsxrO2ksDzxK2uI+Sy4WPVNFFT9KP4A8MsxrO2ksDzyUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[0.44307166 0.03666524 0.58308905]\n [0.44307166 0.03666524 0.58308905]\n [0.44307166 0.03666524 0.58308905]\n [0.44307166 0.03666524 0.58308905]]",
+        "desired_goal": "[[-0.20945276 -1.557654   -0.45848915]\n [-1.3955889  -0.9216642  -1.452584  ]\n [ 0.14752695  0.4101537   1.397067  ]\n [ 1.0830418   0.7846359  -0.14228813]]",
+        "observation": "[[0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]\n [0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]\n [0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]\n [0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]]"
+    },
+    "_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": "[[-0.02200552 -0.11956349  0.19317149]\n [ 0.05514792  0.02739515  0.2632946 ]\n [ 0.11063018  0.00725445  0.17927292]\n [-0.08220599  0.07667524  0.2159434 ]]",
+        "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////9LAHSUYkMI6/zbZb/+EcCUhpRSlIwBbJRLMowBdJRHQLPm4nLq2Sd1fZQoaAZoCWgPQwiJX7GGixz7v5SGlFKUaBVLMmgWR0Cz5sMghbGFdX2UKGgGaAloD0MIFf2hmSc3C8CUhpRSlGgVSzJoFkdAs+aj19ORDHV9lChoBmgJaA9DCIunHmlwKxfAlIaUUpRoFUsyaBZHQLPmhD1XeWR1fZQoaAZoCWgPQwiRQ8TNqeQCwJSGlFKUaBVLMmgWR0Cz53TUVi4KdX2UKGgGaAloD0MIJlZGI5/X/7+UhpRSlGgVSzJoFkdAs+dVhJAdGXV9lChoBmgJaA9DCIi7ehUZrRbAlIaUUpRoFUsyaBZHQLPnNklu3tt1fZQoaAZoCWgPQwgJxsGlY87vv5SGlFKUaBVLMmgWR0Cz5xY24uscdX2UKGgGaAloD0MIdQDEXb1qHMCUhpRSlGgVSzJoFkdAs+hgvDgqE3V9lChoBmgJaA9DCPHW+bfL3gXAlIaUUpRoFUsyaBZHQLPoQfRNRFZ1fZQoaAZoCWgPQwikN9xHbg0bwJSGlFKUaBVLMmgWR0Cz6CMSwnpjdX2UKGgGaAloD0MIkxraAGyA/b+UhpRSlGgVSzJoFkdAs+gDZdv863V9lChoBmgJaA9DCMy3Pqw3OhHAlIaUUpRoFUsyaBZHQLPpdlu3trt1fZQoaAZoCWgPQwhGfZI7bEIewJSGlFKUaBVLMmgWR0Cz6Vd1ZDArdX2UKGgGaAloD0MIrmGGxhMB+L+UhpRSlGgVSzJoFkdAs+k5kiD/VHV9lChoBmgJaA9DCLQ7pBgg4RPAlIaUUpRoFUsyaBZHQLPpGgEEC/51fZQoaAZoCWgPQwgA4xk09A8CwJSGlFKUaBVLMmgWR0Cz6n9PDYRNdX2UKGgGaAloD0MInRIQk3CRHMCUhpRSlGgVSzJoFkdAs+pgqXnhbXV9lChoBmgJaA9DCIigavRqAPu/lIaUUpRoFUsyaBZHQLPqQd2PkrB1fZQoaAZoCWgPQwgFNBE2PL0OwJSGlFKUaBVLMmgWR0Cz6iJtWMjvdX2UKGgGaAloD0MIa/EpAMZTCcCUhpRSlGgVSzJoFkdAs+uMkyDZlHV9lChoBmgJaA9DCKyRXWkZafi/lIaUUpRoFUsyaBZHQLPrbcPOIIp1fZQoaAZoCWgPQwjGbTSAt1AXwJSGlFKUaBVLMmgWR0Cz608d5prUdX2UKGgGaAloD0MIWpwxzAna47+UhpRSlGgVSzJoFkdAs+svrfLs8nV9lChoBmgJaA9DCEFEatrFNPa/lIaUUpRoFUsyaBZHQLPskGEf1Yh1fZQoaAZoCWgPQwicNA2K5kH0v5SGlFKUaBVLMmgWR0Cz7HGDcuandX2UKGgGaAloD0MICW8PQkAOFsCUhpRSlGgVSzJoFkdAs+xSvq1PWXV9lChoBmgJaA9DCIy8rIkF3hrAlIaUUpRoFUsyaBZHQLPsMwBHTZx1fZQoaAZoCWgPQwjf/IaJBikWwJSGlFKUaBVLMmgWR0Cz7XTBhx5tdX2UKGgGaAloD0MI2sU0070+FsCUhpRSlGgVSzJoFkdAs+1ViAlOXXV9lChoBmgJaA9DCLLUer/RvhDAlIaUUpRoFUsyaBZHQLPtNkDZDiR1fZQoaAZoCWgPQwhiD+1jBd8UwJSGlFKUaBVLMmgWR0Cz7RYvnKW+dX2UKGgGaAloD0MIr9LddTYkB8CUhpRSlGgVSzJoFkdAs+4em0mdAnV9lChoBmgJaA9DCGt+/KVFvQ7AlIaUUpRoFUsyaBZHQLPt/2DQJHB1fZQoaAZoCWgPQwg2kZkLXL4AwJSGlFKUaBVLMmgWR0Cz7eB1DBuXdX2UKGgGaAloD0MIxJYeTfVkBsCUhpRSlGgVSzJoFkdAs+3Ajnmq53V9lChoBmgJaA9DCMuD9BQ5RP6/lIaUUpRoFUsyaBZHQLPuxmVqveR1fZQoaAZoCWgPQwgjL2tigU8PwJSGlFKUaBVLMmgWR0Cz7qcYqG1ydX2UKGgGaAloD0MI8u8zLhxIBsCUhpRSlGgVSzJoFkdAs+6IOOKfnXV9lChoBmgJaA9DCFfQtMTK6ADAlIaUUpRoFUsyaBZHQLPuaCMxXXB1fZQoaAZoCWgPQwhRMGMK1nj2v5SGlFKUaBVLMmgWR0Cz712SyMUAdX2UKGgGaAloD0MIi/z6ITZ4EcCUhpRSlGgVSzJoFkdAs+8+POpsGnV9lChoBmgJaA9DCIqvdhTnKPq/lIaUUpRoFUsyaBZHQLPvHvX9R791fZQoaAZoCWgPQwijzXFuE279v5SGlFKUaBVLMmgWR0Cz7v79hqj8dX2UKGgGaAloD0MItkjajT4m8r+UhpRSlGgVSzJoFkdAs+/x+fAbhnV9lChoBmgJaA9DCCU7NgLxWhjAlIaUUpRoFUsyaBZHQLPv0rqMWGh1fZQoaAZoCWgPQwgDIy9rYkHzv5SGlFKUaBVLMmgWR0Cz77Nz4k/sdX2UKGgGaAloD0MIijkIOlo1CMCUhpRSlGgVSzJoFkdAs++ThLoOhHV9lChoBmgJaA9DCPLtXYO+lAfAlIaUUpRoFUsyaBZHQLPwntCzC1t1fZQoaAZoCWgPQwhiSE4mbhXzv5SGlFKUaBVLMmgWR0Cz8H+qebuudX2UKGgGaAloD0MIOKEQAYdQHsCUhpRSlGgVSzJoFkdAs/Bgy8BdU3V9lChoBmgJaA9DCN+j/nqFhey/lIaUUpRoFUsyaBZHQLPwQMb3oLZ1fZQoaAZoCWgPQwha1ZKOcgAVwJSGlFKUaBVLMmgWR0Cz8TSCSRr8dX2UKGgGaAloD0MIZapgVFKHBcCUhpRSlGgVSzJoFkdAs/EVOj7AL3V9lChoBmgJaA9DCMvVj03yowTAlIaUUpRoFUsyaBZHQLPw9fCAMDx1fZQoaAZoCWgPQwirtMU1PtMLwJSGlFKUaBVLMmgWR0Cz8NX3pOerdX2UKGgGaAloD0MIuAGfH0YoA8CUhpRSlGgVSzJoFkdAs/HLDpC8e3V9lChoBmgJaA9DCNlD+1jBL/+/lIaUUpRoFUsyaBZHQLPxq9If8uV1fZQoaAZoCWgPQwirl99pMjMUwJSGlFKUaBVLMmgWR0Cz8YyYXwb3dX2UKGgGaAloD0MIA7StZp1xCsCUhpRSlGgVSzJoFkdAs/FsipvP1XV9lChoBmgJaA9DCEWeJF0zOQ7AlIaUUpRoFUsyaBZHQLPyZMLF4s51fZQoaAZoCWgPQwjC3O7lPlkBwJSGlFKUaBVLMmgWR0Cz8kV1SwW4dX2UKGgGaAloD0MI/g5FgT7BF8CUhpRSlGgVSzJoFkdAs/ImPwNLDnV9lChoBmgJaA9DCM064/viMhXAlIaUUpRoFUsyaBZHQLPyBkgfU4J1fZQoaAZoCWgPQwjAdjBin4ADwJSGlFKUaBVLMmgWR0Cz8xIUJv5ydX2UKGgGaAloD0MIC34bYrzmFMCUhpRSlGgVSzJoFkdAs/Ly4c3l0nV9lChoBmgJaA9DCAslk1M7YwLAlIaUUpRoFUsyaBZHQLPy0889wFV1fZQoaAZoCWgPQwhQbtv3qL/6v5SGlFKUaBVLMmgWR0Cz8rPWH1vmdX2UKGgGaAloD0MIar5KPnZ3AcCUhpRSlGgVSzJoFkdAs/Opq7Ack3V9lChoBmgJaA9DCCXK3lLOtxPAlIaUUpRoFUsyaBZHQLPzimseXAx1fZQoaAZoCWgPQwhNSkG3lzT8v5SGlFKUaBVLMmgWR0Cz82s0Ltu2dX2UKGgGaAloD0MItTLhl/p5+r+UhpRSlGgVSzJoFkdAs/NLPAwfyXV9lChoBmgJaA9DCN3NUx1ykwLAlIaUUpRoFUsyaBZHQLP0Q5LRKHx1fZQoaAZoCWgPQwjxaOOItagTwJSGlFKUaBVLMmgWR0Cz9CRCx/utdX2UKGgGaAloD0MI1LmilBDs/b+UhpRSlGgVSzJoFkdAs/QE5XEIgXV9lChoBmgJaA9DCG05l+KqwhXAlIaUUpRoFUsyaBZHQLPz5OFQEZB1fZQoaAZoCWgPQwi4HoXrUXj4v5SGlFKUaBVLMmgWR0Cz9Nt1EE1VdX2UKGgGaAloD0MIG4ANiBAXEcCUhpRSlGgVSzJoFkdAs/S8KLKmsXV9lChoBmgJaA9DCIOHad/cLxDAlIaUUpRoFUsyaBZHQLP0nPYWcjJ1fZQoaAZoCWgPQwgm5IOezSoKwJSGlFKUaBVLMmgWR0Cz9HzTz/ZNdX2UKGgGaAloD0MIJqq3BrYKDMCUhpRSlGgVSzJoFkdAs/Vvn+yZ8nV9lChoBmgJaA9DCEiphCf02grAlIaUUpRoFUsyaBZHQLP1UEDQqqh1fZQoaAZoCWgPQwi+pDFaR5X8v5SGlFKUaBVLMmgWR0Cz9TEDEFW5dX2UKGgGaAloD0MIqkiFsYUgAcCUhpRSlGgVSzJoFkdAs/URK/VRUHV9lChoBmgJaA9DCN6NBYVBWQ/AlIaUUpRoFUsyaBZHQLP2Bhm5Dqp1fZQoaAZoCWgPQwjw94vZklXkv5SGlFKUaBVLMmgWR0Cz9ebbHp8ndX2UKGgGaAloD0MIJLTlXIprFcCUhpRSlGgVSzJoFkdAs/XHm2b5M3V9lChoBmgJaA9DCBJqhlRR/Pu/lIaUUpRoFUsyaBZHQLP1p67ulXR1fZQoaAZoCWgPQwhlyLH1DOHwv5SGlFKUaBVLMmgWR0Cz9ptvS+g2dX2UKGgGaAloD0MIW+m12VhpDsCUhpRSlGgVSzJoFkdAs/Z8KE3843V9lChoBmgJaA9DCHReY5eoXgTAlIaUUpRoFUsyaBZHQLP2XOdoWYZ1fZQoaAZoCWgPQwj/lZUmpeAIwJSGlFKUaBVLMmgWR0Cz9jzjBEa3dX2UKGgGaAloD0MIpKfIIeLm+b+UhpRSlGgVSzJoFkdAs/c5ZowmFHV9lChoBmgJaA9DCPKxu0BJQQzAlIaUUpRoFUsyaBZHQLP3Gi0OVgR1fZQoaAZoCWgPQwj5hVeSPGcUwJSGlFKUaBVLMmgWR0Cz9vrxd6cBdX2UKGgGaAloD0MI8+hGWFTEBsCUhpRSlGgVSzJoFkdAs/bbAh0QsnV9lChoBmgJaA9DCOG2tvC8NALAlIaUUpRoFUsyaBZHQLP4KdN34bl1fZQoaAZoCWgPQwi+oIUEjI4IwJSGlFKUaBVLMmgWR0Cz+AuuzQeFdX2UKGgGaAloD0MIsAER4srZDMCUhpRSlGgVSzJoFkdAs/fs6S1VpHV9lChoBmgJaA9DCJRPj20ZkADAlIaUUpRoFUsyaBZHQLP3zjXnQpp1ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 75000,
+    "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-PandaPushDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fc7329f44d165954b7b9ecaa5929cad0a18b38e4794390d406ccb565453d892
+size 44734

a2c-PandaPushDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4ac4946d5864fb47bfd54fd8677ee01dacd08ba88559f19b67dcf49c6859fef
+size 46014

a2c-PandaPushDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaPushDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- 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 0x7f9dbeae5790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9dbeae3fc0>"}, "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": 1500000, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678785114775136859, "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": "[[0.44307166 0.03666524 0.58308905]\n [0.44307166 0.03666524 0.58308905]\n [0.44307166 0.03666524 0.58308905]\n [0.44307166 0.03666524 0.58308905]]", "desired_goal": "[[-0.20945276 -1.557654   -0.45848915]\n [-1.3955889  -0.9216642  -1.452584  ]\n [ 0.14752695  0.4101537   1.397067  ]\n [ 1.0830418   0.7846359  -0.14228813]]", "observation": "[[0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]\n [0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]\n [0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]\n [0.44307166 0.03666524 0.58308905 0.01565518 0.00359799 0.00873862]]"}, "_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": "[[-0.02200552 -0.11956349  0.19317149]\n [ 0.05514792  0.02739515  0.2632946 ]\n [ 0.11063018  0.00725445  0.17927292]\n [-0.08220599  0.07667524  0.2159434 ]]", "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": 75000, "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.4992371018975974, "std_reward": 1.785574597875237, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-14T10:41:04.941064"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b016e80c78b09e42589d407e595bee4fe1aef2cbe928dd1e5afe4947c4fe2022
+size 3212