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: -0.27 +/- 0.14
+      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 sha256:8bf39d4edebc088d3d3fb970ab1372373f5663e64c50384e820a8912f0c85a59
+size 106831

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 0x785995c69120>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x785995c65880>"
+    },
+    "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": 1693363566589877140,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-0.5993135  -0.48450956  0.36216444]\n [ 0.43671322  1.3030806  -1.1374604 ]\n [ 0.28397048 -0.0141396   0.46408817]\n [ 0.28397048 -0.0141396   0.46408817]]",
+        "desired_goal": "[[-0.84043616 -1.5914916   0.5723049 ]\n [ 0.646332    1.4430741  -0.4909832 ]\n [ 0.8012832   0.67755187  1.387253  ]\n [ 0.98148906 -1.5833806  -1.4967566 ]]",
+        "observation": "[[-0.5993135  -0.48450956  0.36216444 -0.8350036  -1.6638755   0.89644223]\n [ 0.43671322  1.3030806  -1.1374604   0.8880646   0.78468317 -0.9540029 ]\n [ 0.28397048 -0.0141396   0.46408817  0.48947528 -0.00561343  0.39008635]\n [ 0.28397048 -0.0141396   0.46408817  0.48947528 -0.00561343  0.39008635]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.07960431 -0.05926189  0.00793779]\n [ 0.10345247  0.03043274  0.06731325]\n [-0.09497546 -0.01981335  0.2143585 ]\n [ 0.11759084  0.05013753  0.13103616]]",
+        "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv8peeFtbcGmMAWyUSwOMAXSUR0CmAwf0ulGgdX2UKGgGR7+ndfsu3+dcaAdLAWgIR0CmA2lhG6PKdX2UKGgGR7/ASRr8BMi9aAdLAmgIR0CmArNipeeGdX2UKGgGR7/CBhhH9WIXaAdLAmgIR0CmArudf9gndX2UKGgGR7/Xm4y44Ia+aAdLBWgIR0CmAmR+8XendX2UKGgGR7/YMpgCwKSgaAdLBGgIR0CmA3w84giedX2UKGgGR7/RqQA+6iCbaAdLA2gIR0CmAsosqaw2dX2UKGgGR7/MD9OymhugaAdLA2gIR0CmAnLvb48EdX2UKGgGR7/Q7/GVAzHkaAdLA2gIR0CmA4jLB9CvdX2UKGgGR7/jM0P6KtPpaAdLCGgIR0CmAyvPTodNdX2UKGgGR7+uf5DZ13dLaAdLAmgIR0CmAtMGHHmzdX2UKGgGR7+9wrDqGDcuaAdLAmgIR0CmAnupsGgSdX2UKGgGR7+VVcUuctoSaAdLAWgIR0CmAzJz90ihdX2UKGgGR7/BNh3JPqLTaAdLAmgIR0CmAt23Sa3JdX2UKGgGR7+9/axoqTbGaAdLAmgIR0CmAoZLRKHxdX2UKGgGR7/REmICU5dXaAdLA2gIR0CmA5f4REncdX2UKGgGR7/BXCCSRr8BaAdLAmgIR0CmAzr/KhcrdX2UKGgGR7/HODaoMrmRaAdLA2gIR0CmAum4iHIqdX2UKGgGR7/UdPtUn5SFaAdLA2gIR0CmA6ZoXbdrdX2UKGgGR7/Q04R28qWkaAdLBGgIR0CmApjWTX8PdX2UKGgGR7/cpMHryDqXaAdLBGgIR0CmA01tfoicdX2UKGgGR7/Ms+3Ytg8baAdLA2gIR0CmA7JRwZO0dX2UKGgGR7+/vLHMlkYoaAdLAmgIR0CmA1VMdtEYdX2UKGgGR7/TU3n6l+EzaAdLBGgIR0CmAvxeLNwBdX2UKGgGR7/ONsFdLQHBaAdLA2gIR0CmAqTz3AVPdX2UKGgGR7+zjOs1baAXaAdLAmgIR0CmA7xwZOzqdX2UKGgGR7/I8wpON5t4aAdLA2gIR0CmA2Mg+yJLdX2UKGgGR7/Od8Rcu8K5aAdLA2gIR0CmAwo6CDmKdX2UKGgGR7/Lcu8K5TZQaAdLA2gIR0CmArM9B8hLdX2UKGgGR7+ioMrmQr+YaAdLAWgIR0CmAw8OLBKudX2UKGgGR7+/tF8XvYvnaAdLAmgIR0CmArwNb1RMdX2UKGgGR7/WxnWattALaAdLBGgIR0CmA84KIBRydX2UKGgGR7/MpXp4bCJoaAdLA2gIR0CmA3EWZZ0TdX2UKGgGR7/Ca1kUbkwOaAdLAmgIR0CmAxg9V3lkdX2UKGgGR7+xxzaK1og3aAdLAmgIR0CmA9hyKekIdX2UKGgGR7/DTiKiwjdIaAdLAmgIR0CmA3t+1Bt2dX2UKGgGR7/Q3azu4PPLaAdLA2gIR0CmAssbvPTodX2UKGgGR7/HtOVPepGXaAdLA2gIR0CmAybwjMV2dX2UKGgGR7/S46wMYuTSaAdLA2gIR0CmA+ZuyeI3dX2UKGgGR7/WxQSBbwBpaAdLBGgIR0CmA5AIIF/ydX2UKGgGR7/WeyiVSn+AaAdLBGgIR0CmAuAZ88cNdX2UKGgGR7/Z5PuXu3MIaAdLBGgIR0CmAzwxnFo+dX2UKGgGR7/Oo3Jgb6xgaAdLA2gIR0CmA/cPnSv1dX2UKGgGR7++KpDNQj2SaAdLAmgIR0CmAuoq9XcQdX2UKGgGR7+u2b5M10koaAdLAmgIR0CmA0bz06HTdX2UKGgGR7/WXnhbW3BpaAdLBGgIR0CmA6QyhzvJdX2UKGgGR7/J8GcFyJbdaAdLA2gIR0CmBAhzvJA/dX2UKGgGR7+6vHLidat+aAdLAmgIR0CmA1Kj8DSxdX2UKGgGR7/NUwSJ0nw5aAdLA2gIR0CmAvt8eCCjdX2UKGgGR7+zzBhx5s0paAdLAmgIR0CmBBKYZ2pydX2UKGgGR7+/UhFEy+HraAdLAmgIR0CmAwXlCCz1dX2UKGgGR7/UEWZZ0SyuaAdLBGgIR0CmA7rdvbXZdX2UKGgGR7/Mndfsu3+daAdLA2gIR0CmA2JSiudPdX2UKGgGR7/CmwaBI4EPaAdLAmgIR0CmBB0Bfa6CdX2UKGgGR7/QbB42S+xoaAdLA2gIR0CmAxacRUWEdX2UKGgGR7/Tglnh86V/aAdLA2gIR0CmA8uUUwi8dX2UKGgGR7/QvmYBvJiiaAdLA2gIR0CmA3M3ZPEbdX2UKGgGR7/QC/oJRfnfaAdLA2gIR0CmBC3cQAdXdX2UKGgGR7/Kcz67/XGwaAdLA2gIR0CmAyTi0fHQdX2UKGgGR7/HiRW912aEaAdLA2gIR0CmA9me+VTrdX2UKGgGR7/LrJKaoddWaAdLA2gIR0CmA4EAHVwxdX2UKGgGR7/SZ62OQyRCaAdLA2gIR0CmBD2sJY1YdX2UKGgGR7/Tla8pTdcjaAdLA2gIR0CmA+kxqO94dX2UKGgGR7/RLLIPsiSraAdLA2gIR0CmA5BjWkJsdX2UKGgGR7/YShakhzNmaAdLBGgIR0CmAzlJYkmhdX2UKGgGR7/RFEAo5PuYaAdLA2gIR0CmBEuoo/iYdX2UKGgGR7/D7qptJnQIaAdLAmgIR0CmA/VaW5YpdX2UKGgGR7+5jqfOD8LsaAdLAmgIR0CmA51WCEpRdX2UKGgGR7+2de6Zpi7TaAdLAmgIR0CmA0aw2VFAdX2UKGgGR7+/foA4n4O+aAdLAmgIR0CmA1RLkCFLdX2UKGgGR7/JcEeQuEmIaAdLA2gIR0CmBAm/nGKidX2UKGgGR7/PqTKT0QK8aAdLA2gIR0CmA7EJ8fFKdX2UKGgGR7/g3eFcpsoEaAdLBWgIR0CmBGwVsUItdX2UKGgGR7/EVYZEUj9oaAdLAmgIR0CmBHawt8NQdX2UKGgGR7/Rk/KQq7ROaAdLA2gIR0CmA8EHD766dX2UKGgGR7/fpwS8J2MbaAdLBGgIR0CmA2mwqy4XdX2UKGgGR7/UxW1c+qzaaAdLBGgIR0CmBB7Tc6/7dX2UKGgGR7+5x82Jiy6daAdLAmgIR0CmBCb1AZ88dX2UKGgGR7/RYWtU4rBkaAdLA2gIR0CmA3asySFHdX2UKGgGR7/Y9Net0V8DaAdLBGgIR0CmA9Lk0aZQdX2UKGgGR7/YBsANoakzaAdLBWgIR0CmBI9a+vhZdX2UKGgGR7+/qSowVTJhaAdLAmgIR0CmBDJUPxx2dX2UKGgGR7/DIjGDL8rJaAdLAmgIR0CmA91f/m1ZdX2UKGgGR7/PRR/EwWWQaAdLA2gIR0CmA4Y0Mw10dX2UKGgGR7/CSoOx0MgEaAdLAmgIR0CmBJhhhH9WdX2UKGgGR7/UP420iQkpaAdLA2gIR0CmBD+IEbHZdX2UKGgGR7/GavRqoIfKaAdLA2gIR0CmA5MNDtw8dX2UKGgGR7/Zpwjt5UtJaAdLBGgIR0CmBKsD4gzQdX2UKGgGR7/ITHsC1Z1WaAdLA2gIR0CmBE32dupCdX2UKGgGR7+jsyBTXJ5naAdLAWgIR0CmBFHbypaSdX2UKGgGR7/NBguyu6mPaAdLA2gIR0CmA6G5MDfWdX2UKGgGR7/hSoGY8dPtaAdLB2gIR0CmA/1NxlxwdX2UKGgGR7/Z8FY+0PYnaAdLBGgIR0CmBL0r9VFQdX2UKGgGR7/NLwnYxtYTaAdLA2gIR0CmA6+RYA80dX2UKGgGR7/YAWBSUC7saAdLBGgIR0CmBGRkEs8QdX2UKGgGR7+nywwCbMHKaAdLAWgIR0CmBGg5BC2MdX2UKGgGR7/W/WUbDMvAaAdLBGgIR0CmBA9To+wDdX2UKGgGR7/NQ7cO9WZJaAdLA2gIR0CmBMnOSntOdX2UKGgGR7++r0aqCHymaAdLAmgIR0CmBBeqaPS2dX2UKGgGR7/SNfgJkXk6aAdLBGgIR0CmA8B3aBZqdWUu"
+    },
+    "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:": "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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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce5657fab43bbc33213c870cd83dfa96e435a6a10931459662a59c67a56cd9ad
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b01434c7a5346bd02f3a28d93269b8c7c6402395cf71f84b6d2a7a1ac6182e3
+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 0x785995c69120>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x785995c65880>"}, "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": 1693363566589877140, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.5993135  -0.48450956  0.36216444]\n [ 0.43671322  1.3030806  -1.1374604 ]\n [ 0.28397048 -0.0141396   0.46408817]\n [ 0.28397048 -0.0141396   0.46408817]]", "desired_goal": "[[-0.84043616 -1.5914916   0.5723049 ]\n [ 0.646332    1.4430741  -0.4909832 ]\n [ 0.8012832   0.67755187  1.387253  ]\n [ 0.98148906 -1.5833806  -1.4967566 ]]", "observation": "[[-0.5993135  -0.48450956  0.36216444 -0.8350036  -1.6638755   0.89644223]\n [ 0.43671322  1.3030806  -1.1374604   0.8880646   0.78468317 -0.9540029 ]\n [ 0.28397048 -0.0141396   0.46408817  0.48947528 -0.00561343  0.39008635]\n [ 0.28397048 -0.0141396   0.46408817  0.48947528 -0.00561343  0.39008635]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.07960431 -0.05926189  0.00793779]\n [ 0.10345247  0.03043274  0.06731325]\n [-0.09497546 -0.01981335  0.2143585 ]\n [ 0.11759084  0.05013753  0.13103616]]", "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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"}, "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": -0.27203803360462187, "std_reward": 0.13851005958307108, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-30T03:36:11.584760"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cdddc735aaf7d80f60a23f264cea571783810e2bb6c912d721ce09d3cbff3b03
+size 2623