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: -0.52 +/- 0.16
+      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 sha256:1cc65fa00f9a02581abff102b22c5e8a363d1f8318be2735c49d79a08e27d6c7
+size 108159

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 0x7fe298709120>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fe298702380>"
+    },
+    "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": 1686977322459322721,
+    "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": "[[ 0.36439925 -0.04882754  0.4621086 ]\n [ 0.36439925 -0.04882754  0.4621086 ]\n [ 0.36439925 -0.04882754  0.4621086 ]\n [ 0.36439925 -0.04882754  0.4621086 ]]",
+        "desired_goal": "[[ 0.00780183 -0.91719323  1.065999  ]\n [ 1.6862514  -1.6101338  -1.0727441 ]\n [-1.6099033   0.2928196   0.69469047]\n [ 0.15028621 -0.26611298  0.6342301 ]]",
+        "observation": "[[ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-05]\n [ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-05]\n [ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-05]\n [ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-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": "[[-0.11634555  0.09016854  0.06983174]\n [-0.03425213 -0.06294796  0.18300857]\n [-0.08714081 -0.07278287  0.21745065]\n [-0.04150729  0.11472253  0.06291753]]",
+        "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f797e5ec5770f2b68df885b3901ec95e56a713a0aa68683b0c5915ca41133420
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd31c0135ab4a7225287009d140aa1ba0bae61196a3c9b4084c59d2c703a2887
+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: 3.10.12
+- 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 0x7fe298709120>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe298702380>"}, "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": 1686977322459322721, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAipK6PmT/R72Amew+ipK6PmT/R72Amew+ipK6PmT/R72Amew+ipK6PmT/R72Amew+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAhab/Oy3Nar+ocog/FtfXP90Yzr+uT4m/UBHOv3PslT481zE/oeQZPvY/iL7nXCI/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAACKkro+ZP9HvYCZ7D6DspQ8HigJvPACSriKkro+ZP9HvYCZ7D6DspQ8HigJvPACSriKkro+ZP9HvYCZ7D6DspQ8HigJvPACSriKkro+ZP9HvYCZ7D6DspQ8HigJvPACSriUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.36439925 -0.04882754  0.4621086 ]\n [ 0.36439925 -0.04882754  0.4621086 ]\n [ 0.36439925 -0.04882754  0.4621086 ]\n [ 0.36439925 -0.04882754  0.4621086 ]]", "desired_goal": "[[ 0.00780183 -0.91719323  1.065999  ]\n [ 1.6862514  -1.6101338  -1.0727441 ]\n [-1.6099033   0.2928196   0.69469047]\n [ 0.15028621 -0.26611298  0.6342301 ]]", "observation": "[[ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-05]\n [ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-05]\n [ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-05]\n [ 3.6439925e-01 -4.8827544e-02  4.6210861e-01  1.8151527e-02\n  -8.3713811e-03 -4.8163289e-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": "[[-0.11634555  0.09016854  0.06983174]\n [-0.03425213 -0.06294796  0.18300857]\n [-0.08714081 -0.07278287  0.21745065]\n [-0.04150729  0.11472253  0.06291753]]", "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:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==", "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": "3.10.12", "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": -0.5247079987020697, "std_reward": 0.15969523792775686, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-17T06:28:55.851164"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c07574aff05ac76974121afbc4a7c011e921e76e35009a01f560c8041994d87
+size 2387