Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -16.82 +/- 3.64
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47afa25e2c40724a4c50bec722de98c538548383269a1a7ef5ea88516f0e0eed
+size 106826

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.1.0

a2c-PandaReachDense-v3/data

@@ -0,0 +1,97 @@
+{
+    "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 0x7d97f4d79c60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d97f4d71cc0>"
+    },
+    "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": 10000,
+    "_total_timesteps": 10000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1697042847327487934,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAt8dZP6hAWL74aLI/E+lUPwyzNL5KTR+/POVsP3Kh6D6JRXy/sqROP8QSlD/cCow/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAHwZVvjuPeT4mDz8/IIucv1LTgr+TGE+/6KbCv4LDc7/yQXG/D/jUvpQaUb9X6Tq/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAC3x1k/qEBYvvhosj+rmuG90oyFPm9sBb4T6VQ/DLM0vkpNH7/Dvh4/24/FPgh7OT885Ww/cqHoPolFfL9Qs5M+04r+PfcbmD6ypE4/xBKUP9wKjD8vBrc+j4cQvoIskT+UaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[ 0.85070366 -0.21118414  1.3938284 ]\n [ 0.83168143 -0.17646426 -0.6222731 ]\n [ 0.92537284  0.45435673 -0.985436  ]\n [ 0.80720055  1.1568227   1.0940814 ]]",
+        "desired_goal": "[[-0.20803116  0.24371044  0.7463249 ]\n [-1.2229958  -1.022074   -0.8089687 ]\n [-1.5207186  -0.95220196 -0.9424125 ]\n [-0.41595504 -0.8168118  -0.730123  ]]",
+        "observation": "[[ 0.85070366 -0.21118414  1.3938284  -0.11015829  0.26084    -0.13029645]\n [ 0.83168143 -0.17646426 -0.6222731   0.6200983   0.38586316  0.72453356]\n [ 0.92537284  0.45435673 -0.985436    0.28847742  0.12428822  0.29708835]\n [ 0.80720055  1.1568227   1.0940814   0.35746905 -0.14114211  1.1341708 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.10030423  0.07633635  0.257927  ]\n [-0.07114738 -0.02142948  0.23233038]\n [ 0.14462987  0.0191138   0.1993301 ]\n [ 0.12905926  0.01257783  0.26251128]]",
+        "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": 500,
+    "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 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuDQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3e91412c9a3dbda5e8e0b1cbc439a1662b79648143a59ca272a25f03e17f991
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d52f33994d48a01ffadfa3d9c08acf7a4ad0f0914cc34a4395cd27841d0f4e71
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.25.2

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 0x7d97f4d79c60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d97f4d71cc0>"}, "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": 10000, "_total_timesteps": 10000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697042847327487934, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.85070366 -0.21118414  1.3938284 ]\n [ 0.83168143 -0.17646426 -0.6222731 ]\n [ 0.92537284  0.45435673 -0.985436  ]\n [ 0.80720055  1.1568227   1.0940814 ]]", "desired_goal": "[[-0.20803116  0.24371044  0.7463249 ]\n [-1.2229958  -1.022074   -0.8089687 ]\n [-1.5207186  -0.95220196 -0.9424125 ]\n [-0.41595504 -0.8168118  -0.730123  ]]", "observation": "[[ 0.85070366 -0.21118414  1.3938284  -0.11015829  0.26084    -0.13029645]\n [ 0.83168143 -0.17646426 -0.6222731   0.6200983   0.38586316  0.72453356]\n [ 0.92537284  0.45435673 -0.985436    0.28847742  0.12428822  0.29708835]\n [ 0.80720055  1.1568227   1.0940814   0.35746905 -0.14114211  1.1341708 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.10030423  0.07633635  0.257927  ]\n [-0.07114738 -0.02142948  0.23233038]\n [ 0.14462987  0.0191138   0.1993301 ]\n [ 0.12905926  0.01257783  0.26251128]]", "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": 500, "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 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -16.8150437772274, "std_reward": 3.639090019826134, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-11T16:48:57.098776"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6a46b973b616b8bdf67f402123b7fb510dabf63bdfa23cf51356ebe87297242
+size 2636