Initial commit

README.md

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05b0f815ab75485112f98f56b8302803f33fba807c33f376c391448fd1cce928
+size 2252766

a2c-PandaPickAndPlaceDense-v3/_stable_baselines3_version

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

a2c-PandaPickAndPlaceDense-v3/data

@@ -0,0 +1,102 @@
+{
+    "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 0x7cad8f027b50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7cad8ee2db40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVmQAAAAAAAAB9lCiMCG5ldF9hcmNolF2UKE0AAU0AAU0AAWWMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "net_arch": [
+            256,
+            256,
+            256
+        ],
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "num_timesteps": 100000,
+    "_total_timesteps": 100000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1700050481216048718,
+    "learning_rate": 0.001,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.8709644  -1.3030654   0.10554393]\n [-0.69021887  0.07470028  0.10555046]\n [-0.5849072   0.9868777   0.10555685]\n [ 0.8149216   0.02697708  0.10556017]]",
+        "desired_goal": "[[ 0.5195139  -0.9447423   0.2533464 ]\n [-1.623858    1.052502   -1.0883387 ]\n [ 0.95471096  0.9150096  -0.13013522]\n [ 0.1349099   0.40686584  0.11788772]]",
+        "observation": "[[ 1.0097951   0.11597314 -0.39221627  0.03239728  0.3645347  -1.0648417\n   0.7506138   0.8709644  -1.3030654   0.10554393  0.01220968 -0.02888855\n  -0.03137708  0.03259006 -0.0188836   0.04463288  0.01151701 -0.0203506\n  -0.02124222]\n [-1.2129287   1.5610147   2.3488872  -2.2642367   0.11645991  0.13973203\n  -1.138294   -0.69021887  0.07470028  0.10555046  0.01196677 -0.02870546\n  -0.03149806  0.03285061 -0.01886768  0.04475361  0.01079051 -0.02035079\n  -0.02122434]\n [ 0.45122695 -1.3856189  -0.64140695  0.3355563  -0.04282867  0.11585111\n   1.0076824  -0.5849072   0.9868777   0.10555685  0.01192764 -0.0288601\n  -0.02963562  0.03309323 -0.0183333   0.04475361  0.01079053 -0.02035077\n  -0.02072378]\n [ 0.3266666  -1.5228685  -0.5998298   0.576102    0.15234998  0.13242888\n   1.0076715   0.8149216   0.02697708  0.10556017  0.01195474 -0.02910174\n  -0.03184348  0.03248557 -0.01840895  0.04500295  0.00900701 -0.02293329\n  -0.02137713]]"
+    },
+    "_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": "[[ 0.11950918  0.07454035  0.02      ]\n [-0.06072114  0.03305672  0.02      ]\n [-0.07422308  0.07481163  0.02      ]\n [ 0.12631904 -0.07274652  0.02      ]]",
+        "desired_goal": "[[ 0.08500332 -0.09172739  0.02      ]\n [-0.07154347  0.08310688  0.02      ]\n [ 0.12917748 -0.05931719  0.19062383]\n [-0.00572208  0.0456675   0.02      ]]",
+        "observation": "[[ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00  1.19509175e-01\n   7.45403543e-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 -6.07211404e-02\n   3.30567174e-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 -7.42230788e-02\n   7.48116300e-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  1.26319036e-01\n  -7.27465227e-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]]"
+    },
+    "_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": 1250,
+    "n_steps": 20,
+    "gamma": 0.95,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0001,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": true,
+    "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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"
+    }
+}

a2c-PandaPickAndPlaceDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51caba33ba5be161724dd1410cfb347a65fd81caaa11cc7b95d8062369b560b4
+size 1116195

a2c-PandaPickAndPlaceDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d24e85f8af932be677310984fc421a7eeb0759311cf8facf946cbcb8fc7cfed
+size 1117667

a2c-PandaPickAndPlaceDense-v3/pytorch_variables.pth

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

a2c-PandaPickAndPlaceDense-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: False
+- 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 0x7cad8f027b50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7cad8ee2db40>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVmQAAAAAAAAB9lCiMCG5ldF9hcmNolF2UKE0AAU0AAU0AAWWMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "net_arch": [256, 256, 256], "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 100000, "_total_timesteps": 100000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700050481216048718, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.8709644  -1.3030654   0.10554393]\n [-0.69021887  0.07470028  0.10555046]\n [-0.5849072   0.9868777   0.10555685]\n [ 0.8149216   0.02697708  0.10556017]]", "desired_goal": "[[ 0.5195139  -0.9447423   0.2533464 ]\n [-1.623858    1.052502   -1.0883387 ]\n [ 0.95471096  0.9150096  -0.13013522]\n [ 0.1349099   0.40686584  0.11788772]]", "observation": "[[ 1.0097951   0.11597314 -0.39221627  0.03239728  0.3645347  -1.0648417\n   0.7506138   0.8709644  -1.3030654   0.10554393  0.01220968 -0.02888855\n  -0.03137708  0.03259006 -0.0188836   0.04463288  0.01151701 -0.0203506\n  -0.02124222]\n [-1.2129287   1.5610147   2.3488872  -2.2642367   0.11645991  0.13973203\n  -1.138294   -0.69021887  0.07470028  0.10555046  0.01196677 -0.02870546\n  -0.03149806  0.03285061 -0.01886768  0.04475361  0.01079051 -0.02035079\n  -0.02122434]\n [ 0.45122695 -1.3856189  -0.64140695  0.3355563  -0.04282867  0.11585111\n   1.0076824  -0.5849072   0.9868777   0.10555685  0.01192764 -0.0288601\n  -0.02963562  0.03309323 -0.0183333   0.04475361  0.01079053 -0.02035077\n  -0.02072378]\n [ 0.3266666  -1.5228685  -0.5998298   0.576102    0.15234998  0.13242888\n   1.0076715   0.8149216   0.02697708  0.10556017  0.01195474 -0.02910174\n  -0.03184348  0.03248557 -0.01840895  0.04500295  0.00900701 -0.02293329\n  -0.02137713]]"}, "_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": "[[ 0.11950918  0.07454035  0.02      ]\n [-0.06072114  0.03305672  0.02      ]\n [-0.07422308  0.07481163  0.02      ]\n [ 0.12631904 -0.07274652  0.02      ]]", "desired_goal": "[[ 0.08500332 -0.09172739  0.02      ]\n [-0.07154347  0.08310688  0.02      ]\n [ 0.12917748 -0.05931719  0.19062383]\n [-0.00572208  0.0456675   0.02      ]]", "observation": "[[ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00  0.00000000e+00  1.19509175e-01\n   7.45403543e-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 -6.07211404e-02\n   3.30567174e-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 -7.42230788e-02\n   7.48116300e-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  1.26319036e-01\n  -7.27465227e-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]]"}, "_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": 1250, "n_steps": 20, "gamma": 0.95, "gae_lambda": 0.95, "ent_coef": 0.0001, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": true, "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": "2.1.0+cu118", "GPU Enabled": "False", "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": "2023-11-15T12:21:29.344954"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00c5037a2b9f73c1e7fc1ee6c765b813d21f0a13b7871adb7792db8768622559
+size 3013