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: -2.23 +/- 0.71
+      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 sha256:2d541fc8e0a0c5c37101c32ce488743e3aa88b1cb0e9492f8544d9e2aaec2044
+size 108016

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7ff0a4658940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff0a4653f80>"
+    },
+    "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
+        }
+    },
+    "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,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679201858600451291,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[0.34620437 0.00506577 0.57261556]\n [0.34620437 0.00506577 0.57261556]\n [0.34620437 0.00506577 0.57261556]\n [0.34620437 0.00506577 0.57261556]]",
+        "desired_goal": "[[ 0.66712487 -0.81998426  1.5507091 ]\n [ 1.4853766   1.1631209   0.26034516]\n [-0.9643789   1.0850947  -1.1954527 ]\n [-0.7049458   0.4702549  -1.5623956 ]]",
+        "observation": "[[ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]\n [ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]\n [ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]\n [ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]]"
+    },
+    "_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": "[[-0.04188129  0.09150339  0.26641983]\n [ 0.13299224 -0.128076    0.14944229]\n [-0.01387784 -0.08250846  0.05012006]\n [ 0.13467175 -0.14042467  0.26381874]]",
+        "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,
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69482c9ccf4a4808f63c47cb4784bc1c5a3fed5fb2b6eae45b1f3d92fb31f706
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54f8570d152e9e507bdb5498a6de125d08c879efde7c30fccd23d371ea76eacb
+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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- 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 0x7ff0a4658940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff0a4653f80>"}, "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}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "gAWVUgMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZSMAUOUdJRSlIwEaGlnaJRoHSiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBVLA4WUaCB0lFKUjA1ib3VuZGVkX2JlbG93lGgdKJYDAAAAAAAAAAEBAZRoEowCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIHSUUpSMDWJvdW5kZWRfYWJvdmWUaB0olgMAAAAAAAAAAQEBlGgsSwOFlGggdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZRoIHSUUpRoI2gdKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFUsDhZRoIHSUUpRoKGgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoMmgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoN051YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgVaBhLBoWUaBpoHSiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBVLBoWUaCB0lFKUaCNoHSiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBVLBoWUaCB0lFKUaChoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDJoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDdOdWJ1aBhOaBBOaDdOdWIu", "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, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679201858600451291, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAs0GxPsv+pTvvlhI/s0GxPsv+pTvvlhI/s0GxPsv+pTvvlhI/s0GxPsv+pTvvlhI/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAssgqP33qUb+jfcY/0iC+PyXhlD/2S4U+ieF2v2Lkij+YBJm/VHc0v0DF8D6U/Me/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAACzQbE+y/6lO++WEj9mAsS7tK5FuinrBryzQbE+y/6lO++WEj9mAsS7tK5FuinrBryzQbE+y/6lO++WEj9mAsS7tK5FuinrBryzQbE+y/6lO++WEj9mAsS7tK5FuinrBryUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[0.34620437 0.00506577 0.57261556]\n [0.34620437 0.00506577 0.57261556]\n [0.34620437 0.00506577 0.57261556]\n [0.34620437 0.00506577 0.57261556]]", "desired_goal": "[[ 0.66712487 -0.81998426  1.5507091 ]\n [ 1.4853766   1.1631209   0.26034516]\n [-0.9643789   1.0850947  -1.1954527 ]\n [-0.7049458   0.4702549  -1.5623956 ]]", "observation": "[[ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]\n [ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]\n [ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]\n [ 0.34620437  0.00506577  0.57261556 -0.00598173 -0.0007541  -0.00823478]]"}, "_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": "[[-0.04188129  0.09150339  0.26641983]\n [ 0.13299224 -0.128076    0.14944229]\n [-0.01387784 -0.08250846  0.05012006]\n [ 0.13467175 -0.14042467  0.26381874]]", "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.232007146626711, "std_reward": 0.7072879338872083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-19T05:42:55.639257"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb4b2efd8345a190ece8e80fa2d70d2f71b7f669913c2211e9d94279e776e5ef
+size 3056