Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaPickAndPlace-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaPickAndPlace-v3
+      type: PandaPickAndPlace-v3
+    metrics:
+    - type: mean_reward
+      value: -50.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaPickAndPlace-v3**
+This is a trained model of a **A2C** agent playing **PandaPickAndPlace-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-PandaPickAndPlace-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d083bee8aee3124931b208568007b59e0e4b8057d7723a508806b1725c9d575c
+size 123168

a2c-PandaPickAndPlace-v3/_stable_baselines3_version

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

a2c-PandaPickAndPlace-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 0x7d715351a830>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d7153516740>"
+    },
+    "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": 1695802415777391371,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-0.2513348  -0.7139084   0.16255611]\n [ 0.45193255  0.81944203  0.16256829]\n [ 0.19763088 -0.37545258  0.16256519]\n [ 0.59029114  0.3373381   0.16256829]]",
+        "desired_goal": "[[ 0.50731295  0.5219374  -0.5999059 ]\n [-1.0693494  -0.20935011  1.0551074 ]\n [ 1.4022642   1.719283    1.6240156 ]\n [ 0.07959435  1.4802272  -0.54711443]]",
+        "observation": "[[ 4.3222594e-01 -1.2709374e+00 -7.7068174e-01  9.3318897e-01\n   1.5905491e-01  2.5257918e-01  1.3164718e+00 -2.5133479e-01\n  -7.1390837e-01  1.6255611e-01  5.4229451e-03 -3.2653783e-03\n  -2.4680447e-02  7.2930269e-02 -9.4204411e-02  7.1470469e-02\n   3.4009863e-02 -1.1373939e-02 -1.8101323e-02]\n [ 1.2039685e-01  1.5722712e+00 -7.3255885e-01 -2.6314259e-03\n   5.0005432e-02  8.7403990e-02 -8.5223866e-01  4.5193255e-01\n   8.1944203e-01  1.6256829e-01  5.2108220e-03 -3.2462920e-03\n  -2.3974067e-02  7.3274165e-02 -9.3983822e-02  7.1470469e-02\n   3.4009874e-02 -1.1373928e-02 -1.7772136e-02]\n [ 9.7838324e-01  3.0441815e-01 -7.6884300e-01  1.5522277e-03\n   3.4317988e-01  9.0647116e-02 -8.5114163e-01  1.9763088e-01\n  -3.7545258e-01  1.6256519e-01  5.3150817e-03 -3.2257233e-03\n  -2.2526763e-02  7.3423237e-02 -9.4456494e-02  7.1470469e-02\n   3.4009881e-02 -1.1373959e-02 -1.7612280e-02]\n [ 1.0425870e+00 -3.7789407e-01 -7.6484895e-01  2.4158646e-01\n  -1.0574585e+00  1.7569020e-01  1.1868013e+00  5.9029114e-01\n   3.3733809e-01  1.6256829e-01  5.2831159e-03 -3.3709076e-03\n  -2.5005385e-02  7.2902448e-02 -9.4357327e-02  7.1470469e-02\n   3.4009874e-02 -1.1373927e-02 -1.8315155e-02]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWViwIAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAl6uZvW2qiT0K16M8MluXPbZo5D0K16M82+GGvdOabL0K16M88r9zvTNlB74K16M8lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA4ToBvj1b6T2gv4s9fBH8PXWKkT3kLCs9icgQvWE0RbvViaw9MugcO2oBE70TEF8+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWMAEAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAAAAAAAAl6uZvW2qiT0K16M8AAAAAAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6nIdPRlsGqxDI0o+AAAAAAAAAIAAAAAAAAAAADJblz22aOQ9CtejPAAAAAAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOpyHT0ZbBqsQyNKPgAAAAAAAACAAAAAAAAAAADb4Ya905psvQrXozwAAAAAAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAAAAAAAA8r9zvTNlB74K16M8AAAAAAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlGgOSwRLE4aUaBJ0lFKUdS4=",
+        "achieved_goal": "[[-0.07503431  0.06721959  0.02      ]\n [ 0.07390441  0.11152785  0.02      ]\n [-0.06586047 -0.05776484  0.02      ]\n [-0.05950923 -0.13222198  0.02      ]]",
+        "desired_goal": "[[-0.12620117  0.11394355  0.06823659]\n [ 0.12308022  0.07106487  0.04179086]\n [-0.0353475  -0.0030091   0.08424727]\n [ 0.00239421 -0.03589002  0.21783476]]",
+        "observation": "[[ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00 -7.50343129e-02\n   6.72195926e-02  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00  7.39044100e-02\n   1.11527845e-01  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00 -6.58604726e-02\n  -5.77648394e-02  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00 -5.95092252e-02\n  -1.32221982e-01  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+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, (19,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "gAWVpwEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWBAAAAAAAAAABAQEBlGgIjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKUjA1ib3VuZGVkX2Fib3ZllGgRKJYEAAAAAAAAAAEBAQGUaBVLBIWUaBl0lFKUjAZfc2hhcGWUSwSFlIwDbG93lGgRKJYQAAAAAAAAAAAAgL8AAIC/AACAvwAAgL+UaAtLBIWUaBl0lFKUjARoaWdolGgRKJYQAAAAAAAAAAAAgD8AAIA/AACAPwAAgD+UaAtLBIWUaBl0lFKUjAhsb3dfcmVwcpSMBC0xLjCUjAloaWdoX3JlcHKUjAMxLjCUjApfbnBfcmFuZG9tlE51Yi4=",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_shape": [
+            4
+        ],
+        "low": "[-1. -1. -1. -1.]",
+        "high": "[1. 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-PandaPickAndPlace-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59e10195d351b7bc8e6a1258427e034d046452cf366f1caf17f60c57de143268
+size 51646

a2c-PandaPickAndPlace-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6a102d1f84533849435e7f5db5ceff11326c6ae87af6ed9ab11f43aa01cf648
+size 52926

a2c-PandaPickAndPlace-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaPickAndPlace-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 0x7d715351a830>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d7153516740>"}, "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": 1695802415777391371, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.2513348  -0.7139084   0.16255611]\n [ 0.45193255  0.81944203  0.16256829]\n [ 0.19763088 -0.37545258  0.16256519]\n [ 0.59029114  0.3373381   0.16256829]]", "desired_goal": "[[ 0.50731295  0.5219374  -0.5999059 ]\n [-1.0693494  -0.20935011  1.0551074 ]\n [ 1.4022642   1.719283    1.6240156 ]\n [ 0.07959435  1.4802272  -0.54711443]]", "observation": "[[ 4.3222594e-01 -1.2709374e+00 -7.7068174e-01  9.3318897e-01\n   1.5905491e-01  2.5257918e-01  1.3164718e+00 -2.5133479e-01\n  -7.1390837e-01  1.6255611e-01  5.4229451e-03 -3.2653783e-03\n  -2.4680447e-02  7.2930269e-02 -9.4204411e-02  7.1470469e-02\n   3.4009863e-02 -1.1373939e-02 -1.8101323e-02]\n [ 1.2039685e-01  1.5722712e+00 -7.3255885e-01 -2.6314259e-03\n   5.0005432e-02  8.7403990e-02 -8.5223866e-01  4.5193255e-01\n   8.1944203e-01  1.6256829e-01  5.2108220e-03 -3.2462920e-03\n  -2.3974067e-02  7.3274165e-02 -9.3983822e-02  7.1470469e-02\n   3.4009874e-02 -1.1373928e-02 -1.7772136e-02]\n [ 9.7838324e-01  3.0441815e-01 -7.6884300e-01  1.5522277e-03\n   3.4317988e-01  9.0647116e-02 -8.5114163e-01  1.9763088e-01\n  -3.7545258e-01  1.6256519e-01  5.3150817e-03 -3.2257233e-03\n  -2.2526763e-02  7.3423237e-02 -9.4456494e-02  7.1470469e-02\n   3.4009881e-02 -1.1373959e-02 -1.7612280e-02]\n [ 1.0425870e+00 -3.7789407e-01 -7.6484895e-01  2.4158646e-01\n  -1.0574585e+00  1.7569020e-01  1.1868013e+00  5.9029114e-01\n   3.3733809e-01  1.6256829e-01  5.2831159e-03 -3.3709076e-03\n  -2.5005385e-02  7.2902448e-02 -9.4357327e-02  7.1470469e-02\n   3.4009874e-02 -1.1373927e-02 -1.8315155e-02]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.07503431  0.06721959  0.02      ]\n [ 0.07390441  0.11152785  0.02      ]\n [-0.06586047 -0.05776484  0.02      ]\n [-0.05950923 -0.13222198  0.02      ]]", "desired_goal": "[[-0.12620117  0.11394355  0.06823659]\n [ 0.12308022  0.07106487  0.04179086]\n [-0.0353475  -0.0030091   0.08424727]\n [ 0.00239421 -0.03589002  0.21783476]]", "observation": "[[ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00 -7.50343129e-02\n   6.72195926e-02  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00  7.39044100e-02\n   1.11527845e-01  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00 -6.58604726e-02\n  -5.77648394e-02  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00 -5.95092252e-02\n  -1.32221982e-01  1.99999996e-02  0.00000000e+00 -0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n   0.00000000e+00  0.00000000e+00  0.00000000e+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, (19,), 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  True]", "bounded_above": "[ True  True  True  True]", "_shape": [4], "low": "[-1. -1. -1. -1.]", "high": "[1. 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": -50.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-27T09:10:06.600729"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7bbcb9565089d4ddce30adeaf6108caceaccc46a83dbc69e3e6647304e7f596
+size 3013