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: -1.79 +/- 0.36
+      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:cd09eca95d89a8c7b8cb60fc6ce950c38923d005d7a8e182cc98cc2f9c12125e
+size 108038

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 0x7959e055f490>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7959e0553e00>"
+    },
+    "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": 1691007338630638713,
+    "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": "[[0.3435251  0.01291359 0.5435234 ]\n [0.3435251  0.01291359 0.5435234 ]\n [0.3435251  0.01291359 0.5435234 ]\n [0.3435251  0.01291359 0.5435234 ]]",
+        "desired_goal": "[[ 0.89547414 -0.9367315  -1.330715  ]\n [ 0.5187265  -1.2452434   1.7135191 ]\n [-0.44807312  1.7020966   0.15247756]\n [-0.3919859  -0.46327233  1.5040941 ]]",
+        "observation": "[[0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]\n [0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]\n [0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]\n [0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]]"
+    },
+    "_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": "[[-0.02080082 -0.00752805  0.19480495]\n [-0.02016581  0.12149869  0.13161047]\n [ 0.01096903 -0.1335918   0.11830624]\n [-0.03888027 -0.00739681  0.00234205]]",
+        "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:dc55a72e3ed66bbb86f2cf41d309da8e74997e993e3940199ad987b5f8ba768e
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55f15f31ee2ec4f784b002972a0ec1dbedd1a078584743c9c148e42a2f636750
+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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 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 0x7959e055f490>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7959e0553e00>"}, "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": 1691007338630638713, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAhuKvPoGTUzxZJAs/huKvPoGTUzxZJAs/huKvPoGTUzxZJAs/huKvPoGTUzxZJAs/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAyz1lP6PNb7/eVKq/Q8sEPyNkn7+YVNs/12nlvk3e2T8UIxw+YLLIvggy7b4ohsA/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAACG4q8+gZNTPFkkCz9Lo388g/nQOiJ0WzyG4q8+gZNTPFkkCz9Lo388g/nQOiJ0WzyG4q8+gZNTPFkkCz9Lo388g/nQOiJ0WzyG4q8+gZNTPFkkCz9Lo388g/nQOiJ0WzyUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[0.3435251  0.01291359 0.5435234 ]\n [0.3435251  0.01291359 0.5435234 ]\n [0.3435251  0.01291359 0.5435234 ]\n [0.3435251  0.01291359 0.5435234 ]]", "desired_goal": "[[ 0.89547414 -0.9367315  -1.330715  ]\n [ 0.5187265  -1.2452434   1.7135191 ]\n [-0.44807312  1.7020966   0.15247756]\n [-0.3919859  -0.46327233  1.5040941 ]]", "observation": "[[0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]\n [0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]\n [0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]\n [0.3435251  0.01291359 0.5435234  0.0156029  0.00159435 0.01339439]]"}, "_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": "[[-0.02080082 -0.00752805  0.19480495]\n [-0.02016581  0.12149869  0.13161047]\n [ 0.01096903 -0.1335918   0.11830624]\n [-0.03888027 -0.00739681  0.00234205]]", "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 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": -1.7870832792948932, "std_reward": 0.36485450887143395, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-02T21:29:02.165957"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f11f544d9f612e77e67a7d78b4c6495bbf06b7762ec0d195b090a3625d3fe3c
+size 2387