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: -3.09 +/- 0.52
+      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 sha256:9f90854d34f1e341967e6f74492caab0f29ce177dabeb56c5ded124e6e985249
+size 108282

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:": "gASVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__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 0x7f4a029af680>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f4a02a92ea0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gASVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/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": 1680092746186265894,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gASVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvb3B0L2NvbmRhL2xpYi9weXRob24zLjcvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvb3B0L2NvbmRhL2xpYi9weXRob24zLjcvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP0bwBo24useFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.42096058 -0.04785126  0.42451623]\n [ 0.42096058 -0.04785126  0.42451623]\n [ 0.42096058 -0.04785126  0.42451623]\n [ 0.42096058 -0.04785126  0.42451623]]",
+        "desired_goal": "[[ 0.31491175  1.4555197   0.86601955]\n [-0.2482438   0.2745699   0.12206163]\n [-0.27675906 -0.3770599  -1.2868907 ]\n [-1.535061   -0.683354    0.5880475 ]]",
+        "observation": "[[ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]\n [ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]\n [ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]\n [ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAEBAQGUdJRiLg=="
+    },
+    "_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": "[[-0.08051774 -0.11835551  0.2879286 ]\n [-0.04757556 -0.05121043  0.21048252]\n [ 0.07862092 -0.06983093  0.01833184]\n [ 0.01303391  0.12070953  0.16687688]]",
+        "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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_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:c84ed27596af4ae27bc1bac0da2596a7095b0570a1f71bd0d809c78d9e9e9e21
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c95969bfffe0b9a025ad06635deb48aec77f288afd34e6a61aa9914eeca8852
+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-5.15.89+-x86_64-with-debian-bullseye-sid # 1 SMP Sat Mar 25 09:11:42 UTC 2023
+- Python: 3.7.12
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__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 0x7f4a029af680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4a02a92ea0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/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:": "gASVngMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlGgRjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwOFlGgViUMMAAAgwQAAIMEAACDBlHSUYowEaGlnaJRoHWgfSwCFlGghh5RSlChLAUsDhZRoFYlDDAAAIEEAACBBAAAgQZR0lGKMDWJvdW5kZWRfYmVsb3eUaB1oH0sAhZRoIYeUUpQoSwFLA4WUaBKMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGKJQwMBAQGUdJRijA1ib3VuZGVkX2Fib3ZllGgdaB9LAIWUaCGHlFKUKEsBSwOFlGg1iUMDAQEBlHSUYowKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdaB9LAIWUaCGHlFKUKEsBSwOFlGgViUMMAAAgwQAAIMEAACDBlHSUYmgnaB1oH0sAhZRoIYeUUpQoSwFLA4WUaBWJQwwAACBBAAAgQQAAIEGUdJRiaC5oHWgfSwCFlGghh5RSlChLAUsDhZRoNYlDAwEBAZR0lGJoOmgdaB9LAIWUaCGHlFKUKEsBSwOFlGg1iUMDAQEBlHSUYmhBTnVijAtvYnNlcnZhdGlvbpRoDSmBlH2UKGgQaBVoGEsGhZRoGmgdaB9LAIWUaCGHlFKUKEsBSwaFlGgViUMYAAAgwQAAIMEAACDBAAAgwQAAIMEAACDBlHSUYmgnaB1oH0sAhZRoIYeUUpQoSwFLBoWUaBWJQxgAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUdJRiaC5oHWgfSwCFlGghh5RSlChLAUsGhZRoNYlDBgEBAQEBAZR0lGJoOmgdaB9LAIWUaCGHlFKUKEsBSwaFlGg1iUMGAQEBAQEBlHSUYmhBTnVidWgYTmgQTmhBTnViLg==", "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": 1680092746186265894, "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": "[[ 0.42096058 -0.04785126  0.42451623]\n [ 0.42096058 -0.04785126  0.42451623]\n [ 0.42096058 -0.04785126  0.42451623]\n [ 0.42096058 -0.04785126  0.42451623]]", "desired_goal": "[[ 0.31491175  1.4555197   0.86601955]\n [-0.2482438   0.2745699   0.12206163]\n [-0.27675906 -0.3770599  -1.2868907 ]\n [-1.535061   -0.683354    0.5880475 ]]", "observation": "[[ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]\n [ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]\n [ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]\n [ 0.42096058 -0.04785126  0.42451623  0.0147303  -0.00418614  0.00270267]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAEBAQGUdJRiLg=="}, "_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": "[[-0.08051774 -0.11835551  0.2879286 ]\n [-0.04757556 -0.05121043  0.21048252]\n [ 0.07862092 -0.06983093  0.01833184]\n [ 0.01303391  0.12070953  0.16687688]]", "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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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-5.15.89+-x86_64-with-debian-bullseye-sid # 1 SMP Sat Mar 25 09:11:42 UTC 2023", "Python": "3.7.12", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -3.085520903673023, "std_reward": 0.5150026132223511, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-29T13:45:11.106257"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:368ccc3b275e7b52916855c2f0285cba941df726791d76465cf3186bf2ee314d
+size 3731