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 sha256:403bb0c6756aa75eacf9ca24c25671994726d29157333f2f8ef04364c3ac7ed0
+size 106916

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 0x7a64db8c9240>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7a64db8c1a80>"
+    },
+    "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": 1696060059984087724,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-1.3230892   1.3747633   0.6641748 ]\n [ 0.22239876  0.02230921  0.45084068]\n [ 0.22239876  0.02230921  0.45084068]\n [-0.07621852  0.4495503  -0.16950548]]",
+        "desired_goal": "[[-0.4746645   1.4864142   0.9073204 ]\n [ 0.02447747 -1.3756784  -0.32908732]\n [-0.55570054 -0.3483241   1.4719641 ]\n [ 0.21046726  1.3595372  -1.0070388 ]]",
+        "observation": "[[-1.3230892e+00  1.3747633e+00  6.6417480e-01 -8.7389213e-01\n   1.0807811e+00  1.6017327e+00]\n [ 2.2239876e-01  2.2309210e-02  4.5084068e-01  4.5782110e-01\n  -1.6757314e-03  3.7483770e-01]\n [ 2.2239876e-01  2.2309210e-02  4.5084068e-01  4.5782110e-01\n  -1.6757314e-03  3.7483770e-01]\n [-7.6218516e-02  4.4955030e-01 -1.6950548e-01 -1.7502588e+00\n   1.6366388e+00 -1.3404282e+00]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAQCUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.12433385  0.03230846  0.21879807]\n [ 0.06451957 -0.00736387  0.12404652]\n [ 0.07579758 -0.09903388  0.24362828]\n [ 0.07572474 -0.04369221  0.0173576 ]]",
+        "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:": "gAWVsAMAAAAAAACMFWd5bW5hc2l1bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwUZ3ltbmFzaXVtLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowNYm91bmRlZF9iZWxvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYDAAAAAAAAAAEBAZRoE4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoHCiWAwAAAAAAAAABAQGUaCBLA4WUaCR0lFKUjAZfc2hhcGWUSwOFlIwDbG93lGgcKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZRoJHSUUpSMBGhpZ2iUaBwolgwAAAAAAAAAAAAgQQAAIEEAACBBlGgWSwOFlGgkdJRSlIwIbG93X3JlcHKUjAUtMTAuMJSMCWhpZ2hfcmVwcpSMBDEwLjCUjApfbnBfcmFuZG9tlE51YowMZGVzaXJlZF9nb2FslGgNKYGUfZQoaBBoFmgZaBwolgMAAAAAAAAAAQEBlGggSwOFlGgkdJRSlGgnaBwolgMAAAAAAAAAAQEBlGggSwOFlGgkdJRSlGgsSwOFlGguaBwolgwAAAAAAAAAAAAgwQAAIMEAACDBlGgWSwOFlGgkdJRSlGgzaBwolgwAAAAAAAAAAAAgQQAAIEEAACBBlGgWSwOFlGgkdJRSlGg4jAUtMTAuMJRoOowEMTAuMJRoPE51YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgWaBloHCiWBgAAAAAAAAABAQEBAQGUaCBLBoWUaCR0lFKUaCdoHCiWBgAAAAAAAAABAQEBAQGUaCBLBoWUaCR0lFKUaCxLBoWUaC5oHCiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBZLBoWUaCR0lFKUaDNoHCiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBZLBoWUaCR0lFKUaDiMBS0xMC4wlGg6jAQxMC4wlGg8TnVidWgsTmgQTmg8TnViLg==",
+        "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:f94bee8d32b9bbb6f8239f2776dc0ea23f13786b3ffcf2cfc791130757988f58
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:674b27876ace540d248f1315a9c33692630bc51b68f25ed2f5224885942d8dd3
+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 0x7a64db8c9240>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a64db8c1a80>"}, "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": 1696060059984087724, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA/Vqpvz74rz9cByo/gLxjPs7BtjyX1OY+gLxjPs7BtjyX1OY+dBicvXUr5j7Yki2+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAOgfzvtJCvj8mRmg//ITIPDsWsL8ifqi+ZEIOv4lXsr5Sabw/u4RXPlEFrj+m5oC/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAD9Wqm/PvivP1wHKj9lt1+/CVeKP5QFzT+AvGM+zsG2PJfU5j6HZ+o+N6Tburrqvz6AvGM+zsG2PJfU5j6HZ+o+N6Tburrqvz50GJy9dSvmPtiSLb57COC/YX3RPyeTq7+UaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[-1.3230892   1.3747633   0.6641748 ]\n [ 0.22239876  0.02230921  0.45084068]\n [ 0.22239876  0.02230921  0.45084068]\n [-0.07621852  0.4495503  -0.16950548]]", "desired_goal": "[[-0.4746645   1.4864142   0.9073204 ]\n [ 0.02447747 -1.3756784  -0.32908732]\n [-0.55570054 -0.3483241   1.4719641 ]\n [ 0.21046726  1.3595372  -1.0070388 ]]", "observation": "[[-1.3230892e+00  1.3747633e+00  6.6417480e-01 -8.7389213e-01\n   1.0807811e+00  1.6017327e+00]\n [ 2.2239876e-01  2.2309210e-02  4.5084068e-01  4.5782110e-01\n  -1.6757314e-03  3.7483770e-01]\n [ 2.2239876e-01  2.2309210e-02  4.5084068e-01  4.5782110e-01\n  -1.6757314e-03  3.7483770e-01]\n [-7.6218516e-02  4.4955030e-01 -1.6950548e-01 -1.7502588e+00\n   1.6366388e+00 -1.3404282e+00]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAQCUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.12433385  0.03230846  0.21879807]\n [ 0.06451957 -0.00736387  0.12404652]\n [ 0.07579758 -0.09903388  0.24362828]\n [ 0.07572474 -0.04369221  0.0173576 ]]", "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv8Wz6ab4Ju6MAWyUSwOMAXSUR0CmfIan752ydX2UKGgGR7+bnHNorWiDaAdLAWgIR0CmfAKoqCpWdX2UKGgGR7/a8Cgbp/wzaAdLBGgIR0Cme8Gois4ldX2UKGgGR7/SS88La24NaAdLA2gIR0CmfE2kadc0dX2UKGgGR7/C3yZrpJPJaAdLAmgIR0Cme8qCYkVvdX2UKGgGR7/IAAhje9BbaAdLA2gIR0CmfJa4UeuFdX2UKGgGR7+hAnlXA/LUaAdLAWgIR0Cme9DpLVWkdX2UKGgGR7/Zn6l+EytWaAdLBGgIR0CmfBcOCoS+dX2UKGgGR7/ME0zj3mFKaAdLA2gIR0CmfKPjXFtLdX2UKGgGR7/TQiRnvlU7aAdLBGgIR0CmfGHBDXvqdX2UKGgGR7/TA3kxREWqaAdLA2gIR0Cme95tm+TNdX2UKGgGR7/T8NhE0BOpaAdLA2gIR0CmfCPxH5JsdX2UKGgGR7++Lgn+hoM8aAdLAmgIR0CmfGxFy7wsdX2UKGgGR7/B7v5P/JeWaAdLAmgIR0Cme+k078vVdX2UKGgGR7/bj4pMHryEaAdLBGgIR0CmfLcbiqACdX2UKGgGR7/X0YTCcf/4aAdLA2gIR0CmfDL8zhxYdX2UKGgGR7/OOQQtjCpFaAdLA2gIR0CmfHlbFCLNdX2UKGgGR7/QHU+cH4XXaAdLA2gIR0Cme/XXqZ+hdX2UKGgGR7+pRl6JIlMRaAdLAWgIR0Cme/n/T9bYdX2UKGgGR7/V2i+L3sX0aAdLBGgIR0CmfMp04iosdX2UKGgGR7/Nzundfsu4aAdLA2gIR0CmfIhWgezVdX2UKGgGR7/WAKv3ai9JaAdLBGgIR0CmfEZl4C6pdX2UKGgGR7/DefqX4TK1aAdLAmgIR0CmfNMDGLk0dX2UKGgGR7/Cqgh8pkPMaAdLAmgIR0CmfJDrJKaodX2UKGgGR7/A4hEBsANoaAdLAmgIR0CmfE8FyJbddX2UKGgGR7/XGqgh8pkPaAdLBGgIR0CmfA4BmwqzdX2UKGgGR7/DP557gKnfaAdLAmgIR0CmfBhreqJedX2UKGgGR7/TWUr08NhFaAdLA2gIR0CmfOJy6tkndX2UKGgGR7/QiG34Kx9oaAdLA2gIR0CmfKB11W8zdX2UKGgGR7/cLAYYR/ViaAdLBGgIR0CmfGJ8neBQdX2UKGgGR7/N5zHS4OMEaAdLA2gIR0CmfCVuaWondX2UKGgGR7/OBPsRg7YDaAdLA2gIR0CmfK1Pva11dX2UKGgGR7/UStvGZNO/aAdLBGgIR0CmfPZQgs9TdX2UKGgGR7/RPVd5Y5ktaAdLA2gIR0CmfHIlD4QCdX2UKGgGR7/BTiKiwjdIaAdLAmgIR0CmfDDqW1MNdX2UKGgGR7+1IsiB5HEuaAdLAmgIR0CmfP9ORDCxdX2UKGgGR7/RBJZntfG/aAdLA2gIR0CmfL08vEjxdX2UKGgGR7+yjbi6xxDLaAdLAmgIR0CmfHte+mFbdX2UKGgGR7+4byYoiLVGaAdLAmgIR0CmfDoTGo73dX2UKGgGR7+9O6/Zdv87aAdLAmgIR0CmfIOZb6gvdX2UKGgGR7+/gKneizsyaAdLAmgIR0CmfEJDmbLEdX2UKGgGR7/NbbDdgv12aAdLA2gIR0CmfQ6Ymb9ZdX2UKGgGR7/XRP420iQlaAdLBGgIR0CmfNE7OmiydX2UKGgGR7+37zkIX0oSaAdLAmgIR0CmfI9ytFKDdX2UKGgGR7+5KraM72csaAdLAmgIR0CmfRhsQ/X5dX2UKGgGR7/LYYixFAmiaAdLA2gIR0CmfFK//NqydX2UKGgGR7+2sHSncclxaAdLAmgIR0CmfJiKR+z/dX2UKGgGR7/RukUKzAvdaAdLA2gIR0CmfN8CgbqAdX2UKGgGR7/SBaLXL/0eaAdLA2gIR0CmfSfyf+S9dX2UKGgGR7/Qdhy8zyjIaAdLA2gIR0CmfKhKDkELdX2UKGgGR7/aAmzByjpLaAdLBGgIR0CmfGdeyAx0dX2UKGgGR7/QhPTG5tm+aAdLA2gIR0CmfO992HLzdX2UKGgGR7/PMdtEXtSiaAdLA2gIR0CmfTZ1V5rydX2UKGgGR7+0WvbGm1pkaAdLAmgIR0CmfPg6U7jldX2UKGgGR7/V3Zwn6VMVaAdLA2gIR0CmfHUwaisXdX2UKGgGR7/V/47A+IM0aAdLBGgIR0CmfL0QCjk/dX2UKGgGR7/LsP8Q7LdOaAdLA2gIR0CmfUWTX8O1dX2UKGgGR7/DK15Sm65HaAdLA2gIR0CmfQdz4k/sdX2UKGgGR7/KT+vQnhKlaAdLA2gIR0CmfIR8UmD2dX2UKGgGR7/P62v0RODbaAdLA2gIR0CmfMuk+HJtdX2UKGgGR7/RDbJwKjSHaAdLA2gIR0CmfVRLCemOdX2UKGgGR7/H3EAHVwxWaAdLA2gIR0CmfRijk+5fdX2UKGgGR7/BrAP/aQFLaAdLAmgIR0CmfNbjLjgidX2UKGgGR7/LkFOfukULaAdLA2gIR0CmfJWcriEQdX2UKGgGR7+T1bqyGBWgaAdLAWgIR0CmfR2Gh24edX2UKGgGR7/SNAkcCHRDaAdLA2gIR0CmfWTE74i5dX2UKGgGR7/RASnLq2SdaAdLA2gIR0CmfKOvllshdX2UKGgGR7/ZIjW07bL2aAdLBGgIR0CmfOm4ZuQ7dX2UKGgGR7/VjwhGH58CaAdLA2gIR0CmfXS1/lQudX2UKGgGR7/YXf642CNCaAdLBGgIR0CmfTKKYRdydX2UKGgGR7+aIFeOXE61aAdLAWgIR0CmfXkHlfZ3dX2UKGgGR7/LQeFL39JjaAdLA2gIR0CmfLM0YTCcdX2UKGgGR7/CfeUILPUsaAdLAmgIR0CmfTrmQr+YdX2UKGgGR7/TOwPiDM/yaAdLA2gIR0CmfPjzyz5XdX2UKGgGR7/TvX9R77bdaAdLA2gIR0CmfYW/8EV4dX2UKGgGR7/BwG4ZuQ6qaAdLAmgIR0CmfUOZCv5hdX2UKGgGR7/BSLqD9OynaAdLAmgIR0CmfQHBk7OndX2UKGgGR7/XzC1qnFYMaAdLBGgIR0CmfMbuDzy0dX2UKGgGR7+9TKkl/pdKaAdLAmgIR0CmfZFj/dZadX2UKGgGR7+1G6PKdQO4aAdLAmgIR0CmfQ1cUucudX2UKGgGR7/IXOW0JF9baAdLA2gIR0CmfVO5z5oHdX2UKGgGR7/EPBi1AqusaAdLAmgIR0CmfNBKlHjIdX2UKGgGR7/E67ulXRw7aAdLAmgIR0CmfRX7cfvGdX2UKGgGR7+ogJTl1bJPaAdLAWgIR0CmfNTch1TzdX2UKGgGR7/MGj9GZuyeaAdLA2gIR0CmfZ7aqS5idX2UKGgGR7/JuBtk4FRpaAdLA2gIR0CmfWNQKrq/dX2UKGgGR7/A5TZQHiWFaAdLAmgIR0CmfN/wRXfZdX2UKGgGR7+6r2g3974SaAdLAmgIR0Cmfanb7CSBdX2UKGgGR7/W1/Ue+23KaAdLA2gIR0CmfSX3xnWbdX2UKGgGR7+2G+K0lZ5iaAdLAmgIR0CmfWwu27WedX2UKGgGR7/A4aP0Zm7KaAdLAmgIR0CmfS5cTrVwdX2UKGgGR7/Fovi97F85aAdLA2gIR0CmfO1D8cdYdX2UKGgGR7/Nu2qkuYhMaAdLA2gIR0CmfbdZ7ojfdX2UKGgGR7/MBwMpgCwKaAdLA2gIR0CmfXuwX668dX2UKGgGR7+6XOW0JF9baAdLAmgIR0CmfPhnzxwydX2UKGgGR7/QljVhCtzTaAdLA2gIR0CmfT4RmK64dX2UKGgGR7/F8rI5o4+9aAdLA2gIR0CmfcbsF+uvdX2UKGgGR7/ORT0g8r7PaAdLA2gIR0CmfQXEAHVxdX2UKGgGR7/dH+Idlum8aAdLBGgIR0CmfY2Ebo8qdX2UKGgGR7/LZOi35N48aAdLA2gIR0CmfUuzY287dWUu"}, "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": -0.21849410412833095, "std_reward": 0.11287815730669924, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-30T08:43:08.797641"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b92e91772723d3f2803cbaadd235f7ba3827bffdc93ad3e780fb59420b948de0
+size 2623