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: -0.22 +/- 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:d5d5d84d15c490d50ce855ac268bf697db716411354d61d3c9c562670ac009c8
+size 106987

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.1.0

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 0x7d12bf87bd90>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d12bf876d80>"
+    },
+    "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": 1693663934013208358,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-2.48398576e-02  4.38574255e-01  5.72003543e-01]\n [ 2.53198177e-01 -2.99020088e-04  4.28341597e-01]\n [ 1.14323270e+00  1.18739569e+00 -9.88531947e-01]\n [ 7.55029082e-01  4.45280343e-01  1.14689216e-01]]",
+        "desired_goal": "[[ 0.27492735  1.5613049   1.5072267 ]\n [ 1.2170157   0.65193754  0.75179607]\n [ 1.144158    0.8823908  -1.1356473 ]\n [ 0.90221703  1.4643089   0.18299344]]",
+        "observation": "[[-2.48398576e-02  4.38574255e-01  5.72003543e-01  3.44865054e-01\n   1.66472256e+00  1.14085186e+00]\n [ 2.53198177e-01 -2.99020088e-04  4.28341597e-01  4.87625808e-01\n  -2.14769528e-03  3.84023219e-01]\n [ 1.14323270e+00  1.18739569e+00 -9.88531947e-01  3.48564208e-01\n   5.37435472e-01 -1.60983193e+00]\n [ 7.55029082e-01  4.45280343e-01  1.14689216e-01  1.78881511e-01\n   1.64093316e+00 -1.53433132e+00]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA4ckqPTXIFr71N509Rp68vDg8Fz2v2Dw8UBMVvT5pFD7CcJQ+X6bmvcb53ryDoeY7lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "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": "[[ 0.04169643 -0.1472481   0.07676689]\n [-0.02302469  0.03692266  0.01152627]\n [-0.03639537  0.14493272  0.28992277]\n [-0.11262202 -0.02721871  0.0070383 ]]",
+        "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv9fX6InBtUKMAWyUSwSMAXSUR0CmHOUD+zdDdX2UKGgGR7/AtHxz7uUmaAdLAmgIR0CmHKpQcghbdX2UKGgGR7+/BeokzGgjaAdLAmgIR0CmHCwBxPwedX2UKGgGR7+6d1+y7f52aAdLAmgIR0CmHO02LpA2dX2UKGgGR7/L2YfGMn7YaAdLA2gIR0CmHHFyq+8HdX2UKGgGR7+oX40uUUwjaAdLAWgIR0CmHDCa7VawdX2UKGgGR7+7M9r433pOaAdLAmgIR0CmHLXLmp2mdX2UKGgGR7/Ps8gZCOWCaAdLA2gIR0CmHICYb83udX2UKGgGR7/O3m3fAKv3aAdLA2gIR0CmHD/MnqmkdX2UKGgGR7/Yftx+8XenaAdLBGgIR0CmHQE4ecQRdX2UKGgGR7/OZCv5gw49aAdLA2gIR0CmHMMRQJokdX2UKGgGR7+bItDlYEGJaAdLAWgIR0CmHIXQMQVcdX2UKGgGR7+4V58jRlYmaAdLAmgIR0CmHM2tEG7jdX2UKGgGR7/LW8yvcJt0aAdLA2gIR0CmHE9/J/5MdX2UKGgGR7/LrE9+w1R+aAdLA2gIR0CmHRCP6sQvdX2UKGgGR7/MM2m51/2CaAdLA2gIR0CmHJUeEIw/dX2UKGgGR7+53iaRZEDyaAdLAmgIR0CmHFhiTdLydX2UKGgGR7/OKrq+rU9ZaAdLA2gIR0CmHNsY2sJZdX2UKGgGR7/LPdl/YrauaAdLA2gIR0CmHR3Lmp2mdX2UKGgGR7/IuctoSL62aAdLA2gIR0CmHKILXtjTdX2UKGgGR7+zdhy8zyjIaAdLAmgIR0CmHGE6DGtIdX2UKGgGR7/BA+IMz/IbaAdLAmgIR0CmHShzFMqSdX2UKGgGR7/SsFt8/lhgaAdLA2gIR0CmHOowVTJhdX2UKGgGR7/Ct9x6v7m/aAdLAmgIR0CmHKz7MxGldX2UKGgGR7/K6tDD0lJIaAdLA2gIR0CmHHDp1RtQdX2UKGgGR7/MhDgIhQnAaAdLA2gIR0CmHTdlEqlQdX2UKGgGR7/TCROk+HJtaAdLA2gIR0CmHLvA44p+dX2UKGgGR7/Zhisny/bkaAdLBGgIR0CmHP+o1k1/dX2UKGgGR7/NY/3WWhRJaAdLA2gIR0CmHIGF8G9pdX2UKGgGR7/HkLhJiAlOaAdLAmgIR0CmHUKc/dIodX2UKGgGR7+I9gWrOqvNaAdLAWgIR0CmHUajesPrdX2UKGgGR7/OWEbo8p1BaAdLA2gIR0CmHMwSi/O/dX2UKGgGR7/XNUfgaWHDaAdLA2gIR0CmHI8bzbvgdX2UKGgGR7/UDfWMCLdfaAdLBGgIR0CmHRHUc4o7dX2UKGgGR7/LyS3b212JaAdLA2gIR0CmHNsIE8q4dX2UKGgGR7/UKc/dIoVmaAdLBGgIR0CmHVr8JlasdX2UKGgGR7/QAZKnNxEOaAdLA2gIR0CmHJ5QP7N0dX2UKGgGR7/I/5+H8CPqaAdLA2gIR0CmHSEXtShrdX2UKGgGR7+u05U96kZaaAdLAmgIR0CmHOPcrRShdX2UKGgGR7+02fkFOfukaAdLAmgIR0CmHKgbADaHdX2UKGgGR7/Q4WDYh+vyaAdLA2gIR0CmHWlEAo5QdX2UKGgGR7/Bmvnr6ciGaAdLAmgIR0CmHSsGgSOBdX2UKGgGR7+To6jnFHawaAdLAWgIR0CmHW/2saKldX2UKGgGR7/MI6bONYKZaAdLA2gIR0CmHPQ7cO9WdX2UKGgGR7+o9C/oJRfnaAdLAWgIR0CmHXRcVxjsdX2UKGgGR7/VF+NLlFMJaAdLA2gIR0CmHLew9q1xdX2UKGgGR7/UaJyhi9ZiaAdLA2gIR0CmHTprtVrAdX2UKGgGR7+ejZcs189faAdLAWgIR0CmHLwOe8PGdX2UKGgGR7/GSdOIqLCOaAdLA2gIR0CmHQGVJL/TdX2UKGgGR7+8kzGgi/wiaAdLAmgIR0CmHMS925hCdX2UKGgGR7/X+qioKlYVaAdLBGgIR0CmHYfxMFlkdX2UKGgGR7/WDmr8zhxYaAdLBGgIR0CmHU1LSNOudX2UKGgGR7/ODoyKvV3EaAdLA2gIR0CmHQ/8/D+BdX2UKGgGR7+7114gRsdlaAdLAmgIR0CmHM8QI2OydX2UKGgGR7/Ua+evpyIYaAdLA2gIR0CmHZRNZeRgdX2UKGgGR7/Uw7kn1FpgaAdLBGgIR0CmHV+5vtMPdX2UKGgGR7/Vj/dZaFEiaAdLBGgIR0CmHOFaSs8xdX2UKGgGR7/OAyVObiIdaAdLA2gIR0CmHaJEYwZgdX2UKGgGR7/cMt9QXQ+maAdLBWgIR0CmHSZTAFgVdX2UKGgGR7/ChUR3/xUeaAdLAmgIR0CmHao2n88+dX2UKGgGR7/RA08/2TPjaAdLA2gIR0CmHO1PepGXdX2UKGgGR7+aC+UQkHD8aAdLAWgIR0CmHa5LZi/gdX2UKGgGR7/SxxkupS75aAdLBGgIR0CmHXAE2YOUdX2UKGgGR7/XT238XN1RaAdLBGgIR0CmHTlyq+8HdX2UKGgGR7/NZV4oqkM1aAdLA2gIR0CmHX9HlOoHdX2UKGgGR7/XTfR/mT1TaAdLBGgIR0CmHQDfWMCLdX2UKGgGR7/Z6GQCCBf8aAdLBGgIR0CmHcI+4b0fdX2UKGgGR7/Q19fCyhSMaAdLA2gIR0CmHUcwHqu9dX2UKGgGR7/A6Mir1dxAaAdLAmgIR0CmHYjc/MW5dX2UKGgGR7/EweNkvsZ6aAdLAmgIR0CmHQqcmShbdX2UKGgGR7++uq3mV7hOaAdLAmgIR0CmHVJtzjm0dX2UKGgGR7/Sehwl0HQhaAdLA2gIR0CmHdJ3X7LudX2UKGgGR7/RREnb7CSBaAdLA2gIR0CmHZf4REncdX2UKGgGR7+81O0svqTsaAdLAmgIR0CmHVqneiztdX2UKGgGR7/Nrqt5le4TaAdLA2gIR0CmHRnQY1pCdX2UKGgGR7+95Qgs9SuRaAdLAmgIR0CmHWKiwjdIdX2UKGgGR7/a384xUNrkaAdLBGgIR0CmHeTzVc2SdX2UKGgGR7/SvvjOs1baaAdLA2gIR0CmHab8vVVhdX2UKGgGR7/Lv8ZUDMePaAdLA2gIR0CmHSio0hvBdX2UKGgGR7+zPQfIS13MaAdLAmgIR0CmHe3Gn4widX2UKGgGR7/NPIGQjlgdaAdLA2gIR0CmHXIpx3mndX2UKGgGR7/Ow482aUiZaAdLA2gIR0CmHbQTEit8dX2UKGgGR7/UbvPTodMkaAdLA2gIR0CmHTXYL9dedX2UKGgGR7+d8zAN5MURaAdLAWgIR0CmHTotlI3BdX2UKGgGR7/POB19v0iAaAdLA2gIR0CmHf2pqASWdX2UKGgGR7/H0lJHy3CsaAdLA2gIR0CmHYIl2NeddX2UKGgGR7/QwljVhCtzaAdLA2gIR0CmHcUkWykcdX2UKGgGR7+87muDBdleaAdLAmgIR0CmHggccU/OdX2UKGgGR7/ELn9vS+g2aAdLAmgIR0CmHYyMLncMdX2UKGgGR7/JrVOKwY+CaAdLA2gIR0CmHUvMr3CbdX2UKGgGR7/Ii35N47iiaAdLA2gIR0CmHdL4WUKRdX2UKGgGR7+6jQAuIyj6aAdLAmgIR0CmHZWxY7q6dX2UKGgGR7+7IEKVpsXSaAdLAmgIR0CmHVTn7pFDdX2UKGgGR7+l+uvECNjtaAdLAWgIR0CmHdoWP91mdX2UKGgGR7/U3iJfpljFaAdLBWgIR0CmHiDhLoOhdX2UKGgGR7/P/KhcqvvCaAdLA2gIR0CmHaU163RYdX2UKGgGR7/FAVwgkka/aAdLA2gIR0CmHWRQrMC+dX2UKGgGR7+m2E0zj3mFaAdLAWgIR0CmHiVf3N9qdX2UKGgGR7+WNzbN8ma6aAdLAWgIR0CmHWiN83MqdX2UKGgGR7/Y75VOsT37aAdLBGgIR0CmHet1hb4bdWUu"
+    },
+    "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 '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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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3df960e80bdad22598e6dbd6d08fef96f95a3177c5b3a3669ce20c1007df7bd3
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f11d925d2f911d100f65de6ffe1192566bb88dd8b71ab5415e642782b147d99
+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: 2.1.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- 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 0x7d12bf87bd90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d12bf876d80>"}, "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": 1693663934013208358, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-2.48398576e-02  4.38574255e-01  5.72003543e-01]\n [ 2.53198177e-01 -2.99020088e-04  4.28341597e-01]\n [ 1.14323270e+00  1.18739569e+00 -9.88531947e-01]\n [ 7.55029082e-01  4.45280343e-01  1.14689216e-01]]", "desired_goal": "[[ 0.27492735  1.5613049   1.5072267 ]\n [ 1.2170157   0.65193754  0.75179607]\n [ 1.144158    0.8823908  -1.1356473 ]\n [ 0.90221703  1.4643089   0.18299344]]", "observation": "[[-2.48398576e-02  4.38574255e-01  5.72003543e-01  3.44865054e-01\n   1.66472256e+00  1.14085186e+00]\n [ 2.53198177e-01 -2.99020088e-04  4.28341597e-01  4.87625808e-01\n  -2.14769528e-03  3.84023219e-01]\n [ 1.14323270e+00  1.18739569e+00 -9.88531947e-01  3.48564208e-01\n   5.37435472e-01 -1.60983193e+00]\n [ 7.55029082e-01  4.45280343e-01  1.14689216e-01  1.78881511e-01\n   1.64093316e+00 -1.53433132e+00]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.04169643 -0.1472481   0.07676689]\n [-0.02302469  0.03692266  0.01152627]\n [-0.03639537  0.14493272  0.28992277]\n [-0.11262202 -0.02721871  0.0070383 ]]", "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv9fX6InBtUKMAWyUSwSMAXSUR0CmHOUD+zdDdX2UKGgGR7/AtHxz7uUmaAdLAmgIR0CmHKpQcghbdX2UKGgGR7+/BeokzGgjaAdLAmgIR0CmHCwBxPwedX2UKGgGR7+6d1+y7f52aAdLAmgIR0CmHO02LpA2dX2UKGgGR7/L2YfGMn7YaAdLA2gIR0CmHHFyq+8HdX2UKGgGR7+oX40uUUwjaAdLAWgIR0CmHDCa7VawdX2UKGgGR7+7M9r433pOaAdLAmgIR0CmHLXLmp2mdX2UKGgGR7/Ps8gZCOWCaAdLA2gIR0CmHICYb83udX2UKGgGR7/O3m3fAKv3aAdLA2gIR0CmHD/MnqmkdX2UKGgGR7/Yftx+8XenaAdLBGgIR0CmHQE4ecQRdX2UKGgGR7/OZCv5gw49aAdLA2gIR0CmHMMRQJokdX2UKGgGR7+bItDlYEGJaAdLAWgIR0CmHIXQMQVcdX2UKGgGR7+4V58jRlYmaAdLAmgIR0CmHM2tEG7jdX2UKGgGR7/LW8yvcJt0aAdLA2gIR0CmHE9/J/5MdX2UKGgGR7/LrE9+w1R+aAdLA2gIR0CmHRCP6sQvdX2UKGgGR7/MM2m51/2CaAdLA2gIR0CmHJUeEIw/dX2UKGgGR7+53iaRZEDyaAdLAmgIR0CmHFhiTdLydX2UKGgGR7/OKrq+rU9ZaAdLA2gIR0CmHNsY2sJZdX2UKGgGR7/LPdl/YrauaAdLA2gIR0CmHR3Lmp2mdX2UKGgGR7/IuctoSL62aAdLA2gIR0CmHKILXtjTdX2UKGgGR7+zdhy8zyjIaAdLAmgIR0CmHGE6DGtIdX2UKGgGR7/BA+IMz/IbaAdLAmgIR0CmHShzFMqSdX2UKGgGR7/SsFt8/lhgaAdLA2gIR0CmHOowVTJhdX2UKGgGR7/Ct9x6v7m/aAdLAmgIR0CmHKz7MxGldX2UKGgGR7/K6tDD0lJIaAdLA2gIR0CmHHDp1RtQdX2UKGgGR7/MhDgIhQnAaAdLA2gIR0CmHTdlEqlQdX2UKGgGR7/TCROk+HJtaAdLA2gIR0CmHLvA44p+dX2UKGgGR7/Zhisny/bkaAdLBGgIR0CmHP+o1k1/dX2UKGgGR7/NY/3WWhRJaAdLA2gIR0CmHIGF8G9pdX2UKGgGR7/HkLhJiAlOaAdLAmgIR0CmHUKc/dIodX2UKGgGR7+I9gWrOqvNaAdLAWgIR0CmHUajesPrdX2UKGgGR7/OWEbo8p1BaAdLA2gIR0CmHMwSi/O/dX2UKGgGR7/XNUfgaWHDaAdLA2gIR0CmHI8bzbvgdX2UKGgGR7/UDfWMCLdfaAdLBGgIR0CmHRHUc4o7dX2UKGgGR7/LyS3b212JaAdLA2gIR0CmHNsIE8q4dX2UKGgGR7/UKc/dIoVmaAdLBGgIR0CmHVr8JlasdX2UKGgGR7/QAZKnNxEOaAdLA2gIR0CmHJ5QP7N0dX2UKGgGR7/I/5+H8CPqaAdLA2gIR0CmHSEXtShrdX2UKGgGR7+u05U96kZaaAdLAmgIR0CmHOPcrRShdX2UKGgGR7+02fkFOfukaAdLAmgIR0CmHKgbADaHdX2UKGgGR7/Q4WDYh+vyaAdLA2gIR0CmHWlEAo5QdX2UKGgGR7/Bmvnr6ciGaAdLAmgIR0CmHSsGgSOBdX2UKGgGR7+To6jnFHawaAdLAWgIR0CmHW/2saKldX2UKGgGR7/MI6bONYKZaAdLA2gIR0CmHPQ7cO9WdX2UKGgGR7+o9C/oJRfnaAdLAWgIR0CmHXRcVxjsdX2UKGgGR7/VF+NLlFMJaAdLA2gIR0CmHLew9q1xdX2UKGgGR7/UaJyhi9ZiaAdLA2gIR0CmHTprtVrAdX2UKGgGR7+ejZcs189faAdLAWgIR0CmHLwOe8PGdX2UKGgGR7/GSdOIqLCOaAdLA2gIR0CmHQGVJL/TdX2UKGgGR7+8kzGgi/wiaAdLAmgIR0CmHMS925hCdX2UKGgGR7/X+qioKlYVaAdLBGgIR0CmHYfxMFlkdX2UKGgGR7/WDmr8zhxYaAdLBGgIR0CmHU1LSNOudX2UKGgGR7/ODoyKvV3EaAdLA2gIR0CmHQ/8/D+BdX2UKGgGR7+7114gRsdlaAdLAmgIR0CmHM8QI2OydX2UKGgGR7/Ua+evpyIYaAdLA2gIR0CmHZRNZeRgdX2UKGgGR7/Uw7kn1FpgaAdLBGgIR0CmHV+5vtMPdX2UKGgGR7/Vj/dZaFEiaAdLBGgIR0CmHOFaSs8xdX2UKGgGR7/OAyVObiIdaAdLA2gIR0CmHaJEYwZgdX2UKGgGR7/cMt9QXQ+maAdLBWgIR0CmHSZTAFgVdX2UKGgGR7/ChUR3/xUeaAdLAmgIR0CmHao2n88+dX2UKGgGR7/RA08/2TPjaAdLA2gIR0CmHO1PepGXdX2UKGgGR7+aC+UQkHD8aAdLAWgIR0CmHa5LZi/gdX2UKGgGR7/SxxkupS75aAdLBGgIR0CmHXAE2YOUdX2UKGgGR7/XT238XN1RaAdLBGgIR0CmHTlyq+8HdX2UKGgGR7/NZV4oqkM1aAdLA2gIR0CmHX9HlOoHdX2UKGgGR7/XTfR/mT1TaAdLBGgIR0CmHQDfWMCLdX2UKGgGR7/Z6GQCCBf8aAdLBGgIR0CmHcI+4b0fdX2UKGgGR7/Q19fCyhSMaAdLA2gIR0CmHUcwHqu9dX2UKGgGR7/A6Mir1dxAaAdLAmgIR0CmHYjc/MW5dX2UKGgGR7/EweNkvsZ6aAdLAmgIR0CmHQqcmShbdX2UKGgGR7++uq3mV7hOaAdLAmgIR0CmHVJtzjm0dX2UKGgGR7/Sehwl0HQhaAdLA2gIR0CmHdJ3X7LudX2UKGgGR7/RREnb7CSBaAdLA2gIR0CmHZf4REncdX2UKGgGR7+81O0svqTsaAdLAmgIR0CmHVqneiztdX2UKGgGR7/Nrqt5le4TaAdLA2gIR0CmHRnQY1pCdX2UKGgGR7+95Qgs9SuRaAdLAmgIR0CmHWKiwjdIdX2UKGgGR7/a384xUNrkaAdLBGgIR0CmHeTzVc2SdX2UKGgGR7/SvvjOs1baaAdLA2gIR0CmHab8vVVhdX2UKGgGR7/Lv8ZUDMePaAdLA2gIR0CmHSio0hvBdX2UKGgGR7+zPQfIS13MaAdLAmgIR0CmHe3Gn4widX2UKGgGR7/NPIGQjlgdaAdLA2gIR0CmHXIpx3mndX2UKGgGR7/Ow482aUiZaAdLA2gIR0CmHbQTEit8dX2UKGgGR7/UbvPTodMkaAdLA2gIR0CmHTXYL9dedX2UKGgGR7+d8zAN5MURaAdLAWgIR0CmHTotlI3BdX2UKGgGR7/POB19v0iAaAdLA2gIR0CmHf2pqASWdX2UKGgGR7/H0lJHy3CsaAdLA2gIR0CmHYIl2NeddX2UKGgGR7/QwljVhCtzaAdLA2gIR0CmHcUkWykcdX2UKGgGR7+87muDBdleaAdLAmgIR0CmHggccU/OdX2UKGgGR7/ELn9vS+g2aAdLAmgIR0CmHYyMLncMdX2UKGgGR7/JrVOKwY+CaAdLA2gIR0CmHUvMr3CbdX2UKGgGR7/Ii35N47iiaAdLA2gIR0CmHdL4WUKRdX2UKGgGR7+6jQAuIyj6aAdLAmgIR0CmHZWxY7q6dX2UKGgGR7+7IEKVpsXSaAdLAmgIR0CmHVTn7pFDdX2UKGgGR7+l+uvECNjtaAdLAWgIR0CmHdoWP91mdX2UKGgGR7/U3iJfpljFaAdLBWgIR0CmHiDhLoOhdX2UKGgGR7/P/KhcqvvCaAdLA2gIR0CmHaU163RYdX2UKGgGR7/FAVwgkka/aAdLA2gIR0CmHWRQrMC+dX2UKGgGR7+m2E0zj3mFaAdLAWgIR0CmHiVf3N9qdX2UKGgGR7+WNzbN8ma6aAdLAWgIR0CmHWiN83MqdX2UKGgGR7/Y75VOsT37aAdLBGgIR0CmHet1hb4bdWUu"}, "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 '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": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.22381184622645378, "std_reward": 0.1115951995941518, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-02T15:01:15.483171"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b4a992f3adcc7c8e1b0f9ec56aabcfdeb4c3df9c8eff61f975d673f4259db8b
+size 2636