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: -0.23 +/- 0.07
+      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 sha256:2059eaff0475d2cfe0d81defb04a9351d173d7be5a5c5a475191f680486f19c6
+size 108129

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 0x78df871ed000>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78df871f45c0>"
+    },
+    "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": 800000,
+    "_total_timesteps": 800000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1698287747741237735,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.23412415  0.01928484  0.450672  ]\n [-0.03001041  0.38310954 -0.19763808]\n [ 1.0005976  -0.48076627  1.3220829 ]\n [ 0.9627536  -0.32332623  0.2806419 ]]",
+        "desired_goal": "[[-1.4334471  -0.72732055 -0.48635775]\n [ 0.38790062  0.8931593  -0.96392304]\n [ 1.693072   -0.32397142  1.5594448 ]\n [ 1.3865145  -1.5146776  -0.23194851]]",
+        "observation": "[[ 0.23412415  0.01928484  0.450672    0.4654008  -0.00220492  0.36747003]\n [-0.03001041  0.38310954 -0.19763808 -1.5417271   1.628595   -1.3078235 ]\n [ 1.0005976  -0.48076627  1.3220829   1.2132784   0.41732952  1.4099872 ]\n [ 0.9627536  -0.32332623  0.2806419   1.5807916  -1.5568055  -1.1412207 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.14629582  0.05515084  0.15218273]\n [ 0.1222099  -0.07369839  0.14510413]\n [-0.11239927  0.01768279  0.2126391 ]\n [ 0.14529157  0.05536994  0.14877209]]",
+        "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv9bva11GLDSMAWyUSwSMAXSUR0Cg2YYecQRPdX2UKGgGR7/OKXOW0JF9aAdLA2gIR0Cg2csIeHSGdX2UKGgGR7+9h3JPqLTAaAdLAmgIR0Cg2gvJ7sv7dX2UKGgGR7/QBlcyFfzCaAdLA2gIR0Cg2VO801qGdX2UKGgGR7/QxX4j8k2QaAdLA2gIR0Cg2ZVgYxcndX2UKGgGR7/OhoM8YAKfaAdLA2gIR0Cg2dfKhcqwdX2UKGgGR7+2plz2exwAaAdLAmgIR0Cg2Vwo1DSgdX2UKGgGR7/G0JF9a2WqaAdLA2gIR0Cg2hiSidrgdX2UKGgGR7+VkMCtA9mpaAdLAWgIR0Cg2dx59mYjdX2UKGgGR7/OCeVcD8tPaAdLA2gIR0Cg2aRVhkRSdX2UKGgGR7++4x1xKg7HaAdLAmgIR0Cg2iMGX5WSdX2UKGgGR7+ev+wTufEoaAdLAWgIR0Cg2ai7btZ3dX2UKGgGR7/Rlgc94eLfaAdLA2gIR0Cg2WulO45MdX2UKGgGR7+i+lCTlkpaaAdLAWgIR0Cg2W/IjnmrdX2UKGgGR7/PIre67NB4aAdLBGgIR0Cg2e/EfkmydX2UKGgGR7/LZzPrv9cbaAdLA2gIR0Cg2jAVO9FndX2UKGgGR7/QZxJd0JWvaAdLA2gIR0Cg2bWSEDhcdX2UKGgGR7+2R8twrDqGaAdLAmgIR0Cg2Xg3Lmp3dX2UKGgGR7/TQNTcZccEaAdLA2gIR0Cg2f8HGCI2dX2UKGgGR7/aw2ETQE6laAdLBGgIR0Cg2kNNBWxRdX2UKGgGR7/X/6wdKdxyaAdLBGgIR0Cg2cjIaLn+dX2UKGgGR7/XvmozeoDQaAdLBGgIR0Cg2YvS+g14dX2UKGgGR7/RtSAH3UQTaAdLA2gIR0Cg2gvfbblBdX2UKGgGR7/BcnmaH9FXaAdLAmgIR0Cg2ZbBoEjgdX2UKGgGR7/Qg/1QIldDaAdLA2gIR0Cg2lMMRYigdX2UKGgGR7/TwFC9h7VsaAdLA2gIR0Cg2djK5kLAdX2UKGgGR7/OhTOxB3RpaAdLA2gIR0Cg2hs98qnWdX2UKGgGR7+4ssg+yJKraAdLAmgIR0Cg2luQp4KQdX2UKGgGR7/UBJ7LMcIaaAdLA2gIR0Cg2aOh0yP/dX2UKGgGR7/TMBZIQOFyaAdLA2gIR0Cg2eWC2+fzdX2UKGgGR7/BKs+3Ytg8aAdLAmgIR0Cg2ma2OQyRdX2UKGgGR7/NmHP/rB0qaAdLA2gIR0Cg2iqqOtGNdX2UKGgGR7+KN2ki2UjcaAdLAWgIR0Cg2eyX+l0pdX2UKGgGR7/AdNnGsFMaaAdLAmgIR0Cg2a/LDAJtdX2UKGgGR7+/hUBGQSzxaAdLAmgIR0Cg2biRfWtmdX2UKGgGR7/KMhHLA57xaAdLA2gIR0Cg2nT1CgK4dX2UKGgGR7/ePJq7AckuaAdLBGgIR0Cg2jzfBN21dX2UKGgGR7/d/7iyY5T7aAdLBGgIR0Cg2f65PM0QdX2UKGgGR7/HKlHjIaLoaAdLA2gIR0Cg2cguRLbpdX2UKGgGR7/U7NB4Uvf1aAdLA2gIR0Cg2oT7MxGldX2UKGgGR7/N7di2DxsmaAdLA2gIR0Cg2g6oESuhdX2UKGgGR7/bjT8YQ8OkaAdLBGgIR0Cg2lEzXSSedX2UKGgGR7/Louf29L6DaAdLA2gIR0Cg2dV7Y02tdX2UKGgGR7/bVlf7aZhKaAdLBGgIR0Cg2piyyD7JdX2UKGgGR7/GPBBRhttRaAdLAmgIR0Cg2lyg5BC2dX2UKGgGR7/Lc4YJmdy1aAdLA2gIR0Cg2h5+YtxudX2UKGgGR7/McYIjW07baAdLA2gIR0Cg2eWRq46PdX2UKGgGR7+3TH80k4WDaAdLAmgIR0Cg2ieC9RJmdX2UKGgGR7/Eb1AZ88cNaAdLA2gIR0Cg2qZZB9kSdX2UKGgGR7/N5hz/6wdKaAdLA2gIR0Cg2mpFspG4dX2UKGgGR7/HgP3BYV7AaAdLA2gIR0Cg2fJWvKU3dX2UKGgGR7+u5tm+TNdJaAdLAmgIR0Cg2nTsyBTXdX2UKGgGR7/Ld8Aq/dqMaAdLA2gIR0Cg2rUxubZwdX2UKGgGR7+zokiUxEfDaAdLAmgIR0Cg2f0xM36zdX2UKGgGR7+9BOYYzi0faAdLAmgIR0Cg2n1hTfixdX2UKGgGR7/BvxYq5LAYaAdLAmgIR0Cg2r2n889wdX2UKGgGR7/Cuq3mV7hOaAdLAmgIR0Cg2oVsLv1EdX2UKGgGR7/cS4vvjOs1aAdLB2gIR0Cg2kdG7SRbdX2UKGgGR7/IeFtbcGkfaAdLA2gIR0Cg2gncDbJwdX2UKGgGR7/DB4Uvf0mMaAdLAmgIR0Cg2shvBJqZdX2UKGgGR7+lWluWKMvRaAdLAWgIR0Cg2k5BkZrIdX2UKGgGR7+a+Jxeb/fgaAdLAWgIR0Cg2lKXF98adX2UKGgGR7/TqC6H0se5aAdLA2gIR0Cg2hlWGRFJdX2UKGgGR7/N7rLQokRjaAdLA2gIR0Cg2tW6TW5IdX2UKGgGR7/VuKoAGSpzaAdLBGgIR0Cg2poHC4z8dX2UKGgGR7/KGwiaAnUlaAdLA2gIR0Cg2l/Ls8gZdX2UKGgGR7+6X6ZYxL00aAdLAmgIR0Cg2uDNyHVPdX2UKGgGR7/grOiWVu76aAdLBGgIR0Cg2i0z9CNTdX2UKGgGR7/XNvOyE+PjaAdLBGgIR0Cg2q145cTrdX2UKGgGR7/PlPrOZ9eAaAdLA2gIR0Cg2m80DU3GdX2UKGgGR7/Q4ku6ErXlaAdLBGgIR0Cg2vKrR0EHdX2UKGgGR7/Dg62fChvjaAdLAmgIR0Cg2rZ7PY4AdX2UKGgGR7/LEDyOJcgRaAdLA2gIR0Cg2jq/VRUFdX2UKGgGR7/NEgGKQ7tBaAdLA2gIR0Cg2n7peNT+dX2UKGgGR7+6ilBQemvXaAdLAmgIR0Cg2v7hm5DrdX2UKGgGR7/NODaoMrmRaAdLA2gIR0Cg2sbNKRMfdX2UKGgGR7/IEzwc5sCUaAdLA2gIR0Cg2kr4N7SidX2UKGgGR7/LGEPDpC8faAdLA2gIR0Cg2ozVMEiddX2UKGgGR7/RHoX9BKL9aAdLA2gIR0Cg2wuUliSadX2UKGgGR7/NFPznRsuWaAdLA2gIR0Cg2tXUYsNEdX2UKGgGR7/Qqu8scyWSaAdLA2gIR0Cg2lq8cuJ2dX2UKGgGR7+9Dqnm7rcCaAdLAmgIR0Cg2xcJdB0IdX2UKGgGR7/PVH4Glhw3aAdLA2gIR0Cg2pyQHRkVdX2UKGgGR7/PWjGkvboKaAdLA2gIR0Cg2uK4QSSNdX2UKGgGR7+/F4s3AEdOaAdLAmgIR0Cg2qR//echdX2UKGgGR7/MvFm4AjptaAdLA2gIR0Cg2yOFxn3+dX2UKGgGR7/X4keIVM24aAdLBGgIR0Cg2mwpON5udX2UKGgGR7/WoDxLCemOaAdLA2gIR0Cg2zPDYRNAdX2UKGgGR7/WIFNcnmaIaAdLBGgIR0Cg2vfgaWHDdX2UKGgGR7/W9BKL876paAdLBGgIR0Cg2rm0u14PdX2UKGgGR7/E43FUADJVaAdLA2gIR0Cg2nxRl6JJdX2UKGgGR7/S0JF9a2WqaAdLA2gIR0Cg20P+4smOdX2UKGgGR7/GFdszl90BaAdLA2gIR0Cg2wgLy+YddX2UKGgGR7/TNliBoVVQaAdLA2gIR0Cg2sntOVPfdX2UKGgGR7/emozeoDPoaAdLBGgIR0Cg2pDAJswddX2UKGgGR7+4EX+ERJ2/aAdLAmgIR0Cg201aOgg6dX2UKGgGR7+zCl7+kxh2aAdLAmgIR0Cg2tMgMc6vdX2UKGgGR7/J9d/rjYI0aAdLA2gIR0Cg2xXC0ngHdX2UKGgGR7+3KU3XI2fkaAdLAmgIR0Cg2poAwPAgdX2UKGgGR7+3fyf+S8raaAdLAmgIR0Cg21ZSNwR5dWUu"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 40000,
+    "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 sha256:3229d378c09a0a4c3ed7b1fd6202bf775dbcd2e96ea3c525b473e24063ca3331
+size 45167

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:911bf7c7bb683e46fb21485803b795b589e065a0ce543b324e6f9dc3ef1b7576
+size 46447

a2c-PandaReachDense-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.1.0+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 0x78df871ed000>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78df871f45c0>"}, "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": 800000, "_total_timesteps": 800000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1698287747741237735, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.23412415  0.01928484  0.450672  ]\n [-0.03001041  0.38310954 -0.19763808]\n [ 1.0005976  -0.48076627  1.3220829 ]\n [ 0.9627536  -0.32332623  0.2806419 ]]", "desired_goal": "[[-1.4334471  -0.72732055 -0.48635775]\n [ 0.38790062  0.8931593  -0.96392304]\n [ 1.693072   -0.32397142  1.5594448 ]\n [ 1.3865145  -1.5146776  -0.23194851]]", "observation": "[[ 0.23412415  0.01928484  0.450672    0.4654008  -0.00220492  0.36747003]\n [-0.03001041  0.38310954 -0.19763808 -1.5417271   1.628595   -1.3078235 ]\n [ 1.0005976  -0.48076627  1.3220829   1.2132784   0.41732952  1.4099872 ]\n [ 0.9627536  -0.32332623  0.2806419   1.5807916  -1.5568055  -1.1412207 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAks4VvtjlYT3K1Rs+L0n6PS/vlr0tlhQ+lzHmvX/bkDwQvlk+UMcUPpfLYj22Vxg+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==", "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": "[[-0.14629582  0.05515084  0.15218273]\n [ 0.1222099  -0.07369839  0.14510413]\n [-0.11239927  0.01768279  0.2126391 ]\n [ 0.14529157  0.05536994  0.14877209]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 40000, "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:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.1.0+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": -0.22660667579621077, "std_reward": 0.07051715941243938, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-26T03:19:29.300518"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8647fba58e304869fff2c6f9355e9fdd684aab2e2d3cc75961525ee00d41a47
+size 2623