Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -28.52 +/- 14.72
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+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-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57a897f3548b6d1c699caaf025674b7e8cd64bbb4c081c7a409ea624a3ddfb22
+size 108147

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f2283106050>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f2283102d00>"
+    },
+    "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": 1685220387188302008,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[0.41772366 0.03220468 0.50935   ]\n [0.41772366 0.03220468 0.50935   ]\n [0.41772366 0.03220468 0.50935   ]\n [0.41772366 0.03220468 0.50935   ]]",
+        "desired_goal": "[[ 1.6881415   0.6579501   0.8919274 ]\n [ 0.2643242   0.3196978  -0.4892226 ]\n [-1.5725777   0.25840437  0.74050474]\n [-0.20459436 -1.1896594   0.05270613]]",
+        "observation": "[[ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]\n [ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]\n [ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]\n [ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.02833177 -0.0130865   0.15670027]\n [ 0.06495585 -0.10489301  0.2698308 ]\n [ 0.00393774  0.1309575   0.01966048]\n [-0.00289724 -0.12645109  0.23041293]]",
+        "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 'gym.spaces.dict.Dict'>",
+        ":serialized:": "gAWVWAMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZSMAUOUdJRSlIwEaGlnaJRoHiiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBZLA4WUaCF0lFKUjA1ib3VuZGVkX2JlbG93lGgeKJYDAAAAAAAAAAEBAZRoE4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIXSUUpSMDWJvdW5kZWRfYWJvdmWUaB4olgMAAAAAAAAAAQEBlGgtSwOFlGghdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBZoGUsDhZRoG2geKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZRoIXSUUpRoJGgeKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFksDhZRoIXSUUpRoKWgeKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoM2geKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoOE51YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgWaBlLBoWUaBtoHiiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBZLBoWUaCF0lFKUaCRoHiiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBZLBoWUaCF0lFKUaCloHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDNoHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDhOdWJ1aBlOaBBOaDhOdWIu",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1fddffcd074a025c7bf53c4aae0a37889965a7aa13b7cb1ba933c811c069d1cf
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c2deeafbf3b6c0c2388fab0d4cc3c46613fa752dc599a4298d9fb0cf51a12d9
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

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

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

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 0x7f2283106050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f2283102d00>"}, "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": 1685220387188302008, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.41772366 0.03220468 0.50935   ]\n [0.41772366 0.03220468 0.50935   ]\n [0.41772366 0.03220468 0.50935   ]\n [0.41772366 0.03220468 0.50935   ]]", "desired_goal": "[[ 1.6881415   0.6579501   0.8919274 ]\n [ 0.2643242   0.3196978  -0.4892226 ]\n [-1.5725777   0.25840437  0.74050474]\n [-0.20459436 -1.1896594   0.05270613]]", "observation": "[[ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]\n [ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]\n [ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]\n [ 4.1772366e-01  3.2204684e-02  5.0935000e-01  8.9102527e-03\n   3.2872451e-03 -9.9274097e-05]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.02833177 -0.0130865   0.15670027]\n [ 0.06495585 -0.10489301  0.2698308 ]\n [ 0.00393774  0.1309575   0.01966048]\n [-0.00289724 -0.12645109  0.23041293]]", "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 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -28.518327380158006, "std_reward": 14.721622129484727, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-27T21:49:25.210522"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea8922972516e1e0621f91c1ea17417b76dc0206e761d8bbadc38febb11f4aff
+size 2387