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 +/- 0.07
+      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 sha256:531b5890d3cc0a47972b271e5a0a257752ffe25511461bcb270c52ea3bfe03bb
+size 106832

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 0x7b8c2182d7e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b8c21844180>"
+    },
+    "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": 1696232734413372837,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.21701485 -0.00754196  0.43160537]\n [-0.92657024 -1.3835982  -1.2780008 ]\n [-0.3091646  -0.47714713  0.27509403]\n [-0.92657024 -1.3835982  -1.2780008 ]]",
+        "desired_goal": "[[ 0.9166635   0.70736617 -1.543792  ]\n [-0.80841315 -1.3742765  -0.9102595 ]\n [-0.3472827  -0.8464533   0.1798239 ]\n [-0.499616   -1.361467   -0.9915143 ]]",
+        "observation": "[[ 0.21701485 -0.00754196  0.43160537  0.4762815  -0.00752743  0.38337412]\n [-0.92657024 -1.3835982  -1.2780008  -0.764644   -0.96326494 -0.94177365]\n [-0.3091646  -0.47714713  0.27509403 -0.5389709  -1.6693449   0.23479417]\n [-0.92657024 -1.3835982  -1.2780008  -0.764644   -0.96326494 -0.94177365]]"
+    },
+    "_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": "[[-0.00353056  0.08961361  0.23301664]\n [-0.10899854 -0.10533664  0.26062223]\n [-0.07378387 -0.13056672  0.03207209]\n [-0.14771463  0.11194279  0.25625324]]",
+        "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": 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 sha256:217428136fb7051a792d18668c18a11ffd930c5c9586baae929b54f2803b900b
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8c689de0663a1347ae17d00598e68582f3e42775df279696ab802f4d2f3a26f
+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-5.15.120+-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: 2.0.1+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 0x7b8c2182d7e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b8c21844180>"}, "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": 1696232734413372837, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.21701485 -0.00754196  0.43160537]\n [-0.92657024 -1.3835982  -1.2780008 ]\n [-0.3091646  -0.47714713  0.27509403]\n [-0.92657024 -1.3835982  -1.2780008 ]]", "desired_goal": "[[ 0.9166635   0.70736617 -1.543792  ]\n [-0.80841315 -1.3742765  -0.9102595 ]\n [-0.3472827  -0.8464533   0.1798239 ]\n [-0.499616   -1.361467   -0.9915143 ]]", "observation": "[[ 0.21701485 -0.00754196  0.43160537  0.4762815  -0.00752743  0.38337412]\n [-0.92657024 -1.3835982  -1.2780008  -0.764644   -0.96326494 -0.94177365]\n [-0.3091646  -0.47714713  0.27509403 -0.5389709  -1.6693449   0.23479417]\n [-0.92657024 -1.3835982  -1.2780008  -0.764644   -0.96326494 -0.94177365]]"}, "_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": "[[-0.00353056  0.08961361  0.23301664]\n [-0.10899854 -0.10533664  0.26062223]\n [-0.07378387 -0.13056672  0.03207209]\n [-0.14771463  0.11194279  0.25625324]]", "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv8Nn003wTduMAWyUSwKMAXSUR0ClX/QzDXOGdX2UKGgGR7+38Kohpxm1aAdLAmgIR0ClX7tDUmUodX2UKGgGR7/KkJKJ2t+1aAdLA2gIR0ClX3zdLxqgdX2UKGgGR7/QZOSGJvYOaAdLA2gIR0ClXz1kMCtBdX2UKGgGR7/MhvBJqZc+aAdLA2gIR0ClYAUFbFCLdX2UKGgGR7+/AgxJul41aAdLAmgIR0ClX8UZvUBodX2UKGgGR7+8++ueSSvDaAdLAmgIR0ClYBAEU0vXdX2UKGgGR7+3P9kz41xbaAdLAmgIR0ClX9ArYoRadX2UKGgGR7/GnqFAVwglaAdLA2gIR0ClX404aP0adX2UKGgGR7/Fc+qzZ6D5aAdLA2gIR0ClX02Op84QdX2UKGgGR7+jIkqtozvaaAdLAWgIR0ClYBUe2d/bdX2UKGgGR7+VXV9Wp6yCaAdLAWgIR0ClYBlNtZV5dX2UKGgGR7+0Kx9oexOdaAdLAmgIR0ClX9lhoduHdX2UKGgGR7/BTNt65XlsaAdLAmgIR0ClX1aQV9F4dX2UKGgGR7/NNtZV4oqkaAdLA2gIR0ClX5rjYI0JdX2UKGgGR7/Yb4rSVnmJaAdLA2gIR0ClX+jdpItldX2UKGgGR7/GsZpBX0XhaAdLA2gIR0ClX2Ynv2GqdX2UKGgGR7/Qhc7hegL7aAdLBGgIR0ClYC2phnandX2UKGgGR7/MSeRPoFFEaAdLA2gIR0ClX6qeK8+SdX2UKGgGR7/RDDTBqKxcaAdLA2gIR0ClX/buDzy0dX2UKGgGR7+5WHUMG5c1aAdLAmgIR0ClX7QTmGM5dX2UKGgGR7/RxM36yjYaaAdLA2gIR0ClX3SWiUPhdX2UKGgGR7/Dy7wrlNlAaAdLA2gIR0ClYD6QvHtGdX2UKGgGR7+5+KCQLeANaAdLAmgIR0ClX7+n62v0dX2UKGgGR7+eB6KLsKLLaAdLAWgIR0ClX8QUHpr2dX2UKGgGR7/M9WZJCjUNaAdLA2gIR0ClX4R0EHMVdX2UKGgGR7/SRRuTA31jaAdLA2gIR0ClYEv8Q7LddX2UKGgGR7/ZC5VfeDWcaAdLBGgIR0ClYAxOUMXrdX2UKGgGR7+Tafzz3AVPaAdLAWgIR0ClX8lhPTG6dX2UKGgGR7+7nPmgam4zaAdLAmgIR0ClYBdnCfpVdX2UKGgGR7/Jew9q1w5vaAdLA2gIR0ClX5SWqtHQdX2UKGgGR7/M8AaNuLrHaAdLA2gIR0ClX9knssxxdX2UKGgGR7+1+XqqwQlKaAdLAmgIR0ClYCCemNzbdX2UKGgGR7/ZKbKA8SwoaAdLBWgIR0ClYGUt7KJVdX2UKGgGR7++S1Vo6CDmaAdLAmgIR0ClX+K33HrAdX2UKGgGR7/Q8jRlYlpoaAdLA2gIR0ClX6PuXu3MdX2UKGgGR7+8tCiRGMGYaAdLAmgIR0ClYHNNi6QOdX2UKGgGR7/BE2Hck+otaAdLAmgIR0ClX/BXCCSSdX2UKGgGR7+4KkVN5+pgaAdLAmgIR0ClX7CLl3hXdX2UKGgGR7+ML0Bfa6BiaAdLAWgIR0ClX/T1TR6XdX2UKGgGR7/bKRuCPIXCaAdLBWgIR0ClYDyu6mO3dX2UKGgGR7/K42CNCJGfaAdLA2gIR0ClYIEcS5AhdX2UKGgGR7/UqFRHf/FSaAdLA2gIR0ClX74wRGtqdX2UKGgGR7/UIeYD1XeWaAdLA2gIR0ClYAL/82rGdX2UKGgGR7/Spu/Dcdo4aAdLA2gIR0ClYE2HDaXbdX2UKGgGR7+59Vmz0HyFaAdLAmgIR0ClX8sRHww1dX2UKGgGR7/JQ6ZH/cWTaAdLA2gIR0ClYJKh11W9dX2UKGgGR7/NJ/XoTwlTaAdLA2gIR0ClYFvZh8YydX2UKGgGR7/ZVW0Z3s5XaAdLBGgIR0ClYBjsUqQSdX2UKGgGR7/UO09hZyMlaAdLA2gIR0ClX9kka/ATdX2UKGgGR7/RA80UGmk4aAdLA2gIR0ClYKDCxeLOdX2UKGgGR7/EYlY2bXpXaAdLAmgIR0ClYGfVy3kQdX2UKGgGR7+/cGkep4r0aAdLAmgIR0ClYKxJmNBGdX2UKGgGR7/HHoX9BKL9aAdLA2gIR0ClYCm6f8MvdX2UKGgGR7+TFQ2uPmxMaAdLAWgIR0ClYC4tpVS5dX2UKGgGR7/cGyon8baRaAdLBGgIR0ClX+5k078vdX2UKGgGR7/GVSn+AEt/aAdLA2gIR0ClYHX+VC5VdX2UKGgGR7/RfWcz67/XaAdLA2gIR0ClYLppeu3ddX2UKGgGR7+1Jul41P30aAdLAmgIR0ClYIE+xGDudX2UKGgGR7/NeIEbHZK4aAdLA2gIR0ClYD5gXuVpdX2UKGgGR7/CUX531SOzaAdLAmgIR0ClYMYPPLPldX2UKGgGR7/bh9b5dnkDaAdLBGgIR0ClYALns9jgdX2UKGgGR7/FNTLns9jgaAdLAmgIR0ClYIo5YHPedX2UKGgGR7/CU3XI2fkFaAdLAmgIR0ClYEdSMtK7dX2UKGgGR7+eWrwOOKfnaAdLAWgIR0ClYI7fgrH3dX2UKGgGR7/V8B+4LCvYaAdLA2gIR0ClYNMzMzMzdX2UKGgGR7/ENrCWNWELaAdLAmgIR0ClYFAyVObidX2UKGgGR7/bsj3VTaTPaAdLBGgIR0ClYBbLlmvodX2UKGgGR7/ONUfgaWHDaAdLA2gIR0ClYJ4sEq2CdX2UKGgGR7+8cIZ62OQyaAdLAmgIR0ClYFtHH3lCdX2UKGgGR7/Pidat9x6waAdLA2gIR0ClYOLnLaEjdX2UKGgGR7+9svZh8YygaAdLAmgIR0ClYB/mDDjzdX2UKGgGR7/RBfrrxAjZaAdLA2gIR0ClYKtHYpUhdX2UKGgGR7/Rv9cbBGhFaAdLA2gIR0ClYGiVbA1vdX2UKGgGR7/RXpGFzuF6aAdLA2gIR0ClYPKKHfuUdX2UKGgGR7+oXj2i+L3saAdLAWgIR0ClYLLlvIfbdX2UKGgGR7/QpWV/tpmFaAdLA2gIR0ClYC/zJ6ppdX2UKGgGR7+l/2Cdz4lAaAdLAWgIR0ClYPdyksSTdX2UKGgGR7+nfIjnmq5taAdLAWgIR0ClYLd7fHghdX2UKGgGR7/DJZGKAJ9iaAdLAmgIR0ClYHSiVSn+dX2UKGgGR7+l5prULDyfaAdLAWgIR0ClYLvjOs1bdX2UKGgGR7+4lfJFLFn7aAdLAmgIR0ClYDj1GsmwdX2UKGgGR7/Immce8wpOaAdLA2gIR0ClYIEm6XjVdX2UKGgGR7+5LGrCFbmmaAdLAmgIR0ClYEFpoK2KdX2UKGgGR7/XmygPEsJ6aAdLBGgIR0ClYQuCoS+QdX2UKGgGR7/K18b70nPWaAdLA2gIR0ClYMuqNp/PdX2UKGgGR7+mDvmYBvJjaAdLAWgIR0ClYIjGLk0adX2UKGgGR7/RPnB+F10UaAdLA2gIR0ClYFHUDuBudX2UKGgGR7/RSCOFQEZBaAdLA2gIR0ClYRluNxVAdX2UKGgGR7/SKf4AS39aaAdLA2gIR0ClYNmUwBYFdX2UKGgGR7/TdHDrJKaoaAdLA2gIR0ClYJbCrLhadX2UKGgGR7+cT8HfMwDeaAdLAWgIR0ClYR54W1twdX2UKGgGR7/AyY5T6zmfaAdLAmgIR0ClYFu8K5TZdX2UKGgGR7/MsAeaKDTSaAdLA2gIR0ClYKdoexOddX2UKGgGR7+0WxhUipvQaAdLAmgIR0ClYGe5Fw1jdX2UKGgGR7/Q4hllK9PDaAdLA2gIR0ClYS9fLLZBdX2UKGgGR7/RRhttQ9A5aAdLBGgIR0ClYO95Y5ktdX2UKGgGR7+EBGQSzw+daAdLAWgIR0ClYGy8rZrYdX2UKGgGR7+z1dxAB1cMaAdLAmgIR0ClYLGTs6aLdX2UKGgGR7++VSn+AEt/aAdLAmgIR0ClYTkytV7ydWUu"}, "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-5.15.120+-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": "2.0.1+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": -0.21698106229305267, "std_reward": 0.07376285450030073, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-02T08:39:46.076354"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c685f7c96ea81a3a0b4e8012437c49ad449003c4e7d61af13f8da4e45a87b008
+size 2636