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: -2.70 +/- 1.08
+      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 sha256:87745a262f0bc382644727c9c4c9f819ac3e51751d18f69643847adbd05fe8af
+size 108063

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 0x7f66d5291430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f66d528f8c0>"
+    },
+    "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": 1680709840328072059,
+    "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": "[[ 0.4096195  -0.00638314  0.55513555]\n [ 0.4096195  -0.00638314  0.55513555]\n [ 0.4096195  -0.00638314  0.55513555]\n [ 0.4096195  -0.00638314  0.55513555]]",
+        "desired_goal": "[[-0.97026765  0.61332774  0.04983701]\n [ 1.1675879  -1.1477296  -1.1214982 ]\n [-0.22061487  1.1791089  -0.9924506 ]\n [ 1.0925333  -0.2102519  -0.9295189 ]]",
+        "observation": "[[ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]\n [ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]\n [ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]\n [ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]]"
+    },
+    "_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": "[[ 8.5440673e-02  1.1828037e-02  1.9279586e-01]\n [ 1.0379340e-01  7.6794133e-02  1.1251183e-01]\n [ 2.0118397e-02  8.4568173e-02  1.3753407e-01]\n [-2.5299519e-02  1.8254844e-04  7.4556351e-02]]",
+        "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:d458c0e9158c45fabe568b3765a206bbacc204f52249ce49454a9e682deb225c
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab1f7d4f05ea5bd4ee0e9fe65b91e8bee425240e6f9600bbe4172e20b1ee412e
+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: 2.0.0+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 0x7f66d5291430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f66d528f8c0>"}, "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": 1680709840328072059, "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": "[[ 0.4096195  -0.00638314  0.55513555]\n [ 0.4096195  -0.00638314  0.55513555]\n [ 0.4096195  -0.00638314  0.55513555]\n [ 0.4096195  -0.00638314  0.55513555]]", "desired_goal": "[[-0.97026765  0.61332774  0.04983701]\n [ 1.1675879  -1.1477296  -1.1214982 ]\n [-0.22061487  1.1791089  -0.9924506 ]\n [ 1.0925333  -0.2102519  -0.9295189 ]]", "observation": "[[ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]\n [ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]\n [ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]\n [ 0.4096195  -0.00638314  0.55513555  0.01140818  0.00337335  0.01208583]]"}, "_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": "[[ 8.5440673e-02  1.1828037e-02  1.9279586e-01]\n [ 1.0379340e-01  7.6794133e-02  1.1251183e-01]\n [ 2.0118397e-02  8.4568173e-02  1.3753407e-01]\n [-2.5299519e-02  1.8254844e-04  7.4556351e-02]]", "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": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.695963873062283, "std_reward": 1.0836610905813147, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T16:45:28.062054"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:757ba3e1c253e552e69aa5da3e8d6d55ad04725c1a8d13cc30832c329595f587
+size 3056