Push LunarLander-v2 model

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 263.30 +/- 12.34
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-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
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7f4efe51bca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4efe51bd30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4efe51bdc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4efe51be50>", "_build": "<function ActorCriticPolicy._build at 0x7f4efe51bee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4efe51bf70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4efe520040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4efe5200d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4efe520160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4efe5201f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4efe520280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4efe520310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4efe51c2a0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676978946947054843, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVZhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIby7+tmcmcUCUhpRSlIwBbJRNlwGMAXSUR0CR1pxnnMdMdX2UKGgGaAloD0MIkKLO3IP8cECUhpRSlGgVS+ZoFkdAkdhd7OVxCXV9lChoBmgJaA9DCPmf/N07KXFAlIaUUpRoFU3iAWgWR0CR2UrEtNBXdX2UKGgGaAloD0MIIjXtYtqbcECUhpRSlGgVTQoBaBZHQJHZlA5aNdZ1fZQoaAZoCWgPQwhjmBO0yW1xQJSGlFKUaBVNEwFoFkdAkdpVpj+aSnV9lChoBmgJaA9DCIfEPZZ+DHFAlIaUUpRoFUv8aBZHQJHbQE2YOUd1fZQoaAZoCWgPQwgIza57K3ZvQJSGlFKUaBVNAQFoFkdAkdteUliSaHV9lChoBmgJaA9DCHaIf9hSTnBAlIaUUpRoFUvyaBZHQJHcPTH80k51fZQoaAZoCWgPQwga+FENu5twQJSGlFKUaBVNOAFoFkdAkd17s8gZCXV9lChoBmgJaA9DCPphhPCoKHJAlIaUUpRoFU0BAWgWR0CR3jdE9dNWdX2UKGgGaAloD0MIutqK/eVFbUCUhpRSlGgVS/ZoFkdAkeDlINEw4HV9lChoBmgJaA9DCNibGJJTI3JAlIaUUpRoFU0VAWgWR0CR43j4pMHsdX2UKGgGaAloD0MIU+knnF00b0CUhpRSlGgVS91oFkdAkeSQjyFwk3V9lChoBmgJaA9DCAJk6NiBTXJAlIaUUpRoFU0EAWgWR0CR5mx6OYICdX2UKGgGaAloD0MI5DEDlfFXbUCUhpRSlGgVTXQBaBZHQJHnFZLZi/h1fZQoaAZoCWgPQwiVRzfCoitvQJSGlFKUaBVNbwFoFkdAkec5IH1OCXV9lChoBmgJaA9DCFGjkGTW0XJAlIaUUpRoFU01AWgWR0CR53FL39JjdX2UKGgGaAloD0MIjzf5LXqKcECUhpRSlGgVTdABaBZHQJHpoCYCyQh1fZQoaAZoCWgPQwjmXIqrSvtyQJSGlFKUaBVN3QFoFkdAkenWd/axo3V9lChoBmgJaA9DCP8gkiHH2HFAlIaUUpRoFU0aAWgWR0CR6oOFQEZBdX2UKGgGaAloD0MIbHcP0D1rcECUhpRSlGgVTRcBaBZHQJHq7gxagVZ1fZQoaAZoCWgPQwi8BKc+kGZEQJSGlFKUaBVLwWgWR0CR6zR6Ww/xdX2UKGgGaAloD0MIhxbZzreTcUCUhpRSlGgVTZcBaBZHQJHtCMhouf51fZQoaAZoCWgPQwjSqSuf5S5yQJSGlFKUaBVNDgFoFkdAke5RqCYkV3V9lChoBmgJaA9DCDdsW5SZW3JAlIaUUpRoFU1TAWgWR0CR7ooexOcldX2UKGgGaAloD0MINgLxun6tcUCUhpRSlGgVTbgBaBZHQJHuwNlRP451fZQoaAZoCWgPQwjfGtgqQWtwQJSGlFKUaBVL/WgWR0CR7slByCFsdX2UKGgGaAloD0MIQznRrkKxb0CUhpRSlGgVS+1oFkdAke7RvitJWnV9lChoBmgJaA9DCLHgfsBDGnJAlIaUUpRoFU2QAmgWR0CR7/OH31zydX2UKGgGaAloD0MIgeuKGaGbcECUhpRSlGgVTS0BaBZHQJHwxt0mtyR1fZQoaAZoCWgPQwj2mbM+5WpyQJSGlFKUaBVNLwJoFkdAkfGYddVvM3V9lChoBmgJaA9DCCzUmuadMHJAlIaUUpRoFUvraBZHQJHyf/echDB1fZQoaAZoCWgPQwiwc9NmnCdyQJSGlFKUaBVNJgFoFkdAkfLbM5fdAXV9lChoBmgJaA9DCBQF+kSeH3NAlIaUUpRoFU18AWgWR0CR84teD3/QdX2UKGgGaAloD0MI0ZUIVH8ncECUhpRSlGgVS+9oFkdAkfSnHBDXv3V9lChoBmgJaA9DCLotkQtOAnBAlIaUUpRoFU1IAWgWR0CR9P6mfoRqdX2UKGgGaAloD0MI7tCwGHWacECUhpRSlGgVS+hoFkdAkfYyFGoaUHV9lChoBmgJaA9DCKqaIOr+D3BAlIaUUpRoFU0DAWgWR0CR9qQA+6iCdX2UKGgGaAloD0MIjiPW4lPIb0CUhpRSlGgVTXMBaBZHQJH25kmQbMp1fZQoaAZoCWgPQwgMsmX5uuVzQJSGlFKUaBVNDwFoFkdAkfc6ohpxm3V9lChoBmgJaA9DCIrpQqy+OnNAlIaUUpRoFU2zAWgWR0CR99h/Aj6fdX2UKGgGaAloD0MIofXwZSIuc0CUhpRSlGgVTS0BaBZHQJH4YgMc6vJ1fZQoaAZoCWgPQwg7cM6I0tBxQJSGlFKUaBVNNQFoFkdAkfiqLwWnCXV9lChoBmgJaA9DCNY3MLlRCWlAlIaUUpRoFU01A2gWR0CR+Ve1KGtZdX2UKGgGaAloD0MIUWaDTHIOcECUhpRSlGgVS/FoFkdAkg0mykbgj3V9lChoBmgJaA9DCOpZEMo7KXJAlIaUUpRoFU0xAWgWR0CSDV2y9mHydX2UKGgGaAloD0MIOV6B6InvckCUhpRSlGgVTVgBaBZHQJINqKvV3EB1fZQoaAZoCWgPQwjCNXf0P2VyQJSGlFKUaBVNKwFoFkdAkg3tNi6QNnV9lChoBmgJaA9DCHaIf9jSnXFAlIaUUpRoFU0GAWgWR0CSEGQ7cO9WdX2UKGgGaAloD0MIs/D1tW64cECUhpRSlGgVTQ4BaBZHQJIScPz4DcN1fZQoaAZoCWgPQwjZJ4Bi5B1wQJSGlFKUaBVNPwFoFkdAkhKZ3LV4HHV9lChoBmgJaA9DCMJqLGFtdG9AlIaUUpRoFU1uAWgWR0CSE2l8w5/9dX2UKGgGaAloD0MIgUOoUrPYcECUhpRSlGgVTRgBaBZHQJIVflLeyiV1fZQoaAZoCWgPQwiAgSBAhvVwQJSGlFKUaBVNOAFoFkdAkhWbo8p1BHV9lChoBmgJaA9DCGixFMkX7HNAlIaUUpRoFU0JAWgWR0CSFanOjZctdX2UKGgGaAloD0MIR8Zq83/XbkCUhpRSlGgVTTUBaBZHQJIV/1h9b5d1fZQoaAZoCWgPQwgmxccnJEpwQJSGlFKUaBVNEAFoFkdAkhZ4uGsV+XV9lChoBmgJaA9DCGFPO/z1c3JAlIaUUpRoFU3FAWgWR0CSFrlFtsN2dX2UKGgGaAloD0MI/aNv0rT6bUCUhpRSlGgVTQUBaBZHQJIXC1G9YfZ1fZQoaAZoCWgPQwjwpIXLqmlyQJSGlFKUaBVL92gWR0CSF6Pd2xIKdX2UKGgGaAloD0MI7UW0HVNHcUCUhpRSlGgVTYkBaBZHQJIYqbI91U51fZQoaAZoCWgPQwhOtRZmIfpwQJSGlFKUaBVL/GgWR0CSGKn7HhjwdX2UKGgGaAloD0MIIEJcOXt3TECUhpRSlGgVS7poFkdAkhjs3AEdNnV9lChoBmgJaA9DCDLKMy+Hlm5AlIaUUpRoFUv6aBZHQJIY9A8jiXJ1fZQoaAZoCWgPQwhlNPJ5xc9uQJSGlFKUaBVNFQFoFkdAkhkkaAFxGXV9lChoBmgJaA9DCP9eCg8a1HJAlIaUUpRoFUv5aBZHQJId5P9DQZ51fZQoaAZoCWgPQwiscTYdASVwQJSGlFKUaBVNDAFoFkdAkh4O717IDHV9lChoBmgJaA9DCA6eCU2SbW9AlIaUUpRoFUvgaBZHQJIfSUNayKN1fZQoaAZoCWgPQwii0LLuH0xvQJSGlFKUaBVL7mgWR0CSH4PYWcjJdX2UKGgGaAloD0MI8BMH0O/yckCUhpRSlGgVS+VoFkdAkiAUDU3GXHV9lChoBmgJaA9DCEtbXOMzRG9AlIaUUpRoFUvpaBZHQJIgmIacZtN1fZQoaAZoCWgPQwhdF35wPrhxQJSGlFKUaBVNWgFoFkdAkiH0KiO/+XV9lChoBmgJaA9DCGCt2jWhyG9AlIaUUpRoFU0EAWgWR0CSIll/H5rQdX2UKGgGaAloD0MIDMwKRfrBcECUhpRSlGgVTSYBaBZHQJIigGxD9fl1fZQoaAZoCWgPQwh0Yg/tI3txQJSGlFKUaBVNKQFoFkdAkiKYis4kvHV9lChoBmgJaA9DCJbNHJJao3FAlIaUUpRoFU0OAWgWR0CSI1OH31zydX2UKGgGaAloD0MIlumXiPdNckCUhpRSlGgVTR4BaBZHQJIkfv7WNFV1fZQoaAZoCWgPQwglPQytzjxzQJSGlFKUaBVNLwFoFkdAkiUDGPxQSHV9lChoBmgJaA9DCIeMR6mE8m9AlIaUUpRoFU0tAWgWR0CSJSzUI9kjdX2UKGgGaAloD0MIYcYUrPGCbECUhpRSlGgVTUsBaBZHQJImFSYPXkJ1fZQoaAZoCWgPQwi+ofDZOnNwQJSGlFKUaBVL+GgWR0CSJ2zFdcB2dX2UKGgGaAloD0MIERyXcZMOc0CUhpRSlGgVS+5oFkdAkighjawljXV9lChoBmgJaA9DCGGOHr+3QGtAlIaUUpRoFU0CAWgWR0CSKUi1RceKdX2UKGgGaAloD0MIatyb3zCTcUCUhpRSlGgVS+toFkdAkioaO5rgwXV9lChoBmgJaA9DCK9DNSWZ33BAlIaUUpRoFUvsaBZHQJIqUk9lmOF1fZQoaAZoCWgPQwg5RUdyeWpxQJSGlFKUaBVNSwFoFkdAkiqYUahpQHV9lChoBmgJaA9DCHYZ/tPNWnFAlIaUUpRoFU0eAWgWR0CSKrjPfKp2dX2UKGgGaAloD0MI/tXjvhXjcECUhpRSlGgVTTwBaBZHQJIq4bXHzYp1fZQoaAZoCWgPQwh9eJYgI6NvQJSGlFKUaBVNGgFoFkdAkitnIMjNZHV9lChoBmgJaA9DCL5p+uwAs3FAlIaUUpRoFU0sAWgWR0CSLE6vaDf4dX2UKGgGaAloD0MIvYqMDkgZb0CUhpRSlGgVTQABaBZHQJIs+rOqvNh1fZQoaAZoCWgPQwgNjLysCc5wQJSGlFKUaBVNbwFoFkdAki96fBeok3V9lChoBmgJaA9DCNoB1xUzKHFAlIaUUpRoFU0GAWgWR0CSMKfK6nR+dX2UKGgGaAloD0MI7YMsC+YFc0CUhpRSlGgVTTgBaBZHQJIw8w1zhgp1fZQoaAZoCWgPQwgHJ6Jf20xwQJSGlFKUaBVNEAFoFkdAkjHQmE4//3V9lChoBmgJaA9DCE637BD/fnFAlIaUUpRoFUvyaBZHQJIzAS39aU11fZQoaAZoCWgPQwhm2v6VVQ1xQJSGlFKUaBVNEAFoFkdAkjMVnuiN83V9lChoBmgJaA9DCIOluoCXPm9AlIaUUpRoFUvyaBZHQJIzbQC0WuZ1fZQoaAZoCWgPQwh5Xb9gN0ZuQJSGlFKUaBVNEgFoFkdAkjQEypJf6XV9lChoBmgJaA9DCHGS5o9pam5AlIaUUpRoFU0GAWgWR0CSNFbC79Q5dWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da6bf654822ba2e0929cfde9ce2f8dc1563fed593f8ec3ead03f9e950a9a77af
+size 147388

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7f4efe51bca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4efe51bd30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4efe51bdc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4efe51be50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4efe51bee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4efe51bf70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4efe520040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4efe5200d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4efe520160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4efe5201f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4efe520280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4efe520310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f4efe51c2a0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676978946947054843,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVZhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIby7+tmcmcUCUhpRSlIwBbJRNlwGMAXSUR0CR1pxnnMdMdX2UKGgGaAloD0MIkKLO3IP8cECUhpRSlGgVS+ZoFkdAkdhd7OVxCXV9lChoBmgJaA9DCPmf/N07KXFAlIaUUpRoFU3iAWgWR0CR2UrEtNBXdX2UKGgGaAloD0MIIjXtYtqbcECUhpRSlGgVTQoBaBZHQJHZlA5aNdZ1fZQoaAZoCWgPQwhjmBO0yW1xQJSGlFKUaBVNEwFoFkdAkdpVpj+aSnV9lChoBmgJaA9DCIfEPZZ+DHFAlIaUUpRoFUv8aBZHQJHbQE2YOUd1fZQoaAZoCWgPQwgIza57K3ZvQJSGlFKUaBVNAQFoFkdAkdteUliSaHV9lChoBmgJaA9DCHaIf9hSTnBAlIaUUpRoFUvyaBZHQJHcPTH80k51fZQoaAZoCWgPQwga+FENu5twQJSGlFKUaBVNOAFoFkdAkd17s8gZCXV9lChoBmgJaA9DCPphhPCoKHJAlIaUUpRoFU0BAWgWR0CR3jdE9dNWdX2UKGgGaAloD0MIutqK/eVFbUCUhpRSlGgVS/ZoFkdAkeDlINEw4HV9lChoBmgJaA9DCNibGJJTI3JAlIaUUpRoFU0VAWgWR0CR43j4pMHsdX2UKGgGaAloD0MIU+knnF00b0CUhpRSlGgVS91oFkdAkeSQjyFwk3V9lChoBmgJaA9DCAJk6NiBTXJAlIaUUpRoFU0EAWgWR0CR5mx6OYICdX2UKGgGaAloD0MI5DEDlfFXbUCUhpRSlGgVTXQBaBZHQJHnFZLZi/h1fZQoaAZoCWgPQwiVRzfCoitvQJSGlFKUaBVNbwFoFkdAkec5IH1OCXV9lChoBmgJaA9DCFGjkGTW0XJAlIaUUpRoFU01AWgWR0CR53FL39JjdX2UKGgGaAloD0MIjzf5LXqKcECUhpRSlGgVTdABaBZHQJHpoCYCyQh1fZQoaAZoCWgPQwjmXIqrSvtyQJSGlFKUaBVN3QFoFkdAkenWd/axo3V9lChoBmgJaA9DCP8gkiHH2HFAlIaUUpRoFU0aAWgWR0CR6oOFQEZBdX2UKGgGaAloD0MIbHcP0D1rcECUhpRSlGgVTRcBaBZHQJHq7gxagVZ1fZQoaAZoCWgPQwi8BKc+kGZEQJSGlFKUaBVLwWgWR0CR6zR6Ww/xdX2UKGgGaAloD0MIhxbZzreTcUCUhpRSlGgVTZcBaBZHQJHtCMhouf51fZQoaAZoCWgPQwjSqSuf5S5yQJSGlFKUaBVNDgFoFkdAke5RqCYkV3V9lChoBmgJaA9DCDdsW5SZW3JAlIaUUpRoFU1TAWgWR0CR7ooexOcldX2UKGgGaAloD0MINgLxun6tcUCUhpRSlGgVTbgBaBZHQJHuwNlRP451fZQoaAZoCWgPQwjfGtgqQWtwQJSGlFKUaBVL/WgWR0CR7slByCFsdX2UKGgGaAloD0MIQznRrkKxb0CUhpRSlGgVS+1oFkdAke7RvitJWnV9lChoBmgJaA9DCLHgfsBDGnJAlIaUUpRoFU2QAmgWR0CR7/OH31zydX2UKGgGaAloD0MIgeuKGaGbcECUhpRSlGgVTS0BaBZHQJHwxt0mtyR1fZQoaAZoCWgPQwj2mbM+5WpyQJSGlFKUaBVNLwJoFkdAkfGYddVvM3V9lChoBmgJaA9DCCzUmuadMHJAlIaUUpRoFUvraBZHQJHyf/echDB1fZQoaAZoCWgPQwiwc9NmnCdyQJSGlFKUaBVNJgFoFkdAkfLbM5fdAXV9lChoBmgJaA9DCBQF+kSeH3NAlIaUUpRoFU18AWgWR0CR84teD3/QdX2UKGgGaAloD0MI0ZUIVH8ncECUhpRSlGgVS+9oFkdAkfSnHBDXv3V9lChoBmgJaA9DCLotkQtOAnBAlIaUUpRoFU1IAWgWR0CR9P6mfoRqdX2UKGgGaAloD0MI7tCwGHWacECUhpRSlGgVS+hoFkdAkfYyFGoaUHV9lChoBmgJaA9DCKqaIOr+D3BAlIaUUpRoFU0DAWgWR0CR9qQA+6iCdX2UKGgGaAloD0MIjiPW4lPIb0CUhpRSlGgVTXMBaBZHQJH25kmQbMp1fZQoaAZoCWgPQwgMsmX5uuVzQJSGlFKUaBVNDwFoFkdAkfc6ohpxm3V9lChoBmgJaA9DCIrpQqy+OnNAlIaUUpRoFU2zAWgWR0CR99h/Aj6fdX2UKGgGaAloD0MIofXwZSIuc0CUhpRSlGgVTS0BaBZHQJH4YgMc6vJ1fZQoaAZoCWgPQwg7cM6I0tBxQJSGlFKUaBVNNQFoFkdAkfiqLwWnCXV9lChoBmgJaA9DCNY3MLlRCWlAlIaUUpRoFU01A2gWR0CR+Ve1KGtZdX2UKGgGaAloD0MIUWaDTHIOcECUhpRSlGgVS/FoFkdAkg0mykbgj3V9lChoBmgJaA9DCOpZEMo7KXJAlIaUUpRoFU0xAWgWR0CSDV2y9mHydX2UKGgGaAloD0MIOV6B6InvckCUhpRSlGgVTVgBaBZHQJINqKvV3EB1fZQoaAZoCWgPQwjCNXf0P2VyQJSGlFKUaBVNKwFoFkdAkg3tNi6QNnV9lChoBmgJaA9DCHaIf9jSnXFAlIaUUpRoFU0GAWgWR0CSEGQ7cO9WdX2UKGgGaAloD0MIs/D1tW64cECUhpRSlGgVTQ4BaBZHQJIScPz4DcN1fZQoaAZoCWgPQwjZJ4Bi5B1wQJSGlFKUaBVNPwFoFkdAkhKZ3LV4HHV9lChoBmgJaA9DCMJqLGFtdG9AlIaUUpRoFU1uAWgWR0CSE2l8w5/9dX2UKGgGaAloD0MIgUOoUrPYcECUhpRSlGgVTRgBaBZHQJIVflLeyiV1fZQoaAZoCWgPQwiAgSBAhvVwQJSGlFKUaBVNOAFoFkdAkhWbo8p1BHV9lChoBmgJaA9DCGixFMkX7HNAlIaUUpRoFU0JAWgWR0CSFanOjZctdX2UKGgGaAloD0MIR8Zq83/XbkCUhpRSlGgVTTUBaBZHQJIV/1h9b5d1fZQoaAZoCWgPQwgmxccnJEpwQJSGlFKUaBVNEAFoFkdAkhZ4uGsV+XV9lChoBmgJaA9DCGFPO/z1c3JAlIaUUpRoFU3FAWgWR0CSFrlFtsN2dX2UKGgGaAloD0MI/aNv0rT6bUCUhpRSlGgVTQUBaBZHQJIXC1G9YfZ1fZQoaAZoCWgPQwjwpIXLqmlyQJSGlFKUaBVL92gWR0CSF6Pd2xIKdX2UKGgGaAloD0MI7UW0HVNHcUCUhpRSlGgVTYkBaBZHQJIYqbI91U51fZQoaAZoCWgPQwhOtRZmIfpwQJSGlFKUaBVL/GgWR0CSGKn7HhjwdX2UKGgGaAloD0MIIEJcOXt3TECUhpRSlGgVS7poFkdAkhjs3AEdNnV9lChoBmgJaA9DCDLKMy+Hlm5AlIaUUpRoFUv6aBZHQJIY9A8jiXJ1fZQoaAZoCWgPQwhlNPJ5xc9uQJSGlFKUaBVNFQFoFkdAkhkkaAFxGXV9lChoBmgJaA9DCP9eCg8a1HJAlIaUUpRoFUv5aBZHQJId5P9DQZ51fZQoaAZoCWgPQwiscTYdASVwQJSGlFKUaBVNDAFoFkdAkh4O717IDHV9lChoBmgJaA9DCA6eCU2SbW9AlIaUUpRoFUvgaBZHQJIfSUNayKN1fZQoaAZoCWgPQwii0LLuH0xvQJSGlFKUaBVL7mgWR0CSH4PYWcjJdX2UKGgGaAloD0MI8BMH0O/yckCUhpRSlGgVS+VoFkdAkiAUDU3GXHV9lChoBmgJaA9DCEtbXOMzRG9AlIaUUpRoFUvpaBZHQJIgmIacZtN1fZQoaAZoCWgPQwhdF35wPrhxQJSGlFKUaBVNWgFoFkdAkiH0KiO/+XV9lChoBmgJaA9DCGCt2jWhyG9AlIaUUpRoFU0EAWgWR0CSIll/H5rQdX2UKGgGaAloD0MIDMwKRfrBcECUhpRSlGgVTSYBaBZHQJIigGxD9fl1fZQoaAZoCWgPQwh0Yg/tI3txQJSGlFKUaBVNKQFoFkdAkiKYis4kvHV9lChoBmgJaA9DCJbNHJJao3FAlIaUUpRoFU0OAWgWR0CSI1OH31zydX2UKGgGaAloD0MIlumXiPdNckCUhpRSlGgVTR4BaBZHQJIkfv7WNFV1fZQoaAZoCWgPQwglPQytzjxzQJSGlFKUaBVNLwFoFkdAkiUDGPxQSHV9lChoBmgJaA9DCIeMR6mE8m9AlIaUUpRoFU0tAWgWR0CSJSzUI9kjdX2UKGgGaAloD0MIYcYUrPGCbECUhpRSlGgVTUsBaBZHQJImFSYPXkJ1fZQoaAZoCWgPQwi+ofDZOnNwQJSGlFKUaBVL+GgWR0CSJ2zFdcB2dX2UKGgGaAloD0MIERyXcZMOc0CUhpRSlGgVS+5oFkdAkighjawljXV9lChoBmgJaA9DCGGOHr+3QGtAlIaUUpRoFU0CAWgWR0CSKUi1RceKdX2UKGgGaAloD0MIatyb3zCTcUCUhpRSlGgVS+toFkdAkioaO5rgwXV9lChoBmgJaA9DCK9DNSWZ33BAlIaUUpRoFUvsaBZHQJIqUk9lmOF1fZQoaAZoCWgPQwg5RUdyeWpxQJSGlFKUaBVNSwFoFkdAkiqYUahpQHV9lChoBmgJaA9DCHYZ/tPNWnFAlIaUUpRoFU0eAWgWR0CSKrjPfKp2dX2UKGgGaAloD0MI/tXjvhXjcECUhpRSlGgVTTwBaBZHQJIq4bXHzYp1fZQoaAZoCWgPQwh9eJYgI6NvQJSGlFKUaBVNGgFoFkdAkitnIMjNZHV9lChoBmgJaA9DCL5p+uwAs3FAlIaUUpRoFU0sAWgWR0CSLE6vaDf4dX2UKGgGaAloD0MIvYqMDkgZb0CUhpRSlGgVTQABaBZHQJIs+rOqvNh1fZQoaAZoCWgPQwgNjLysCc5wQJSGlFKUaBVNbwFoFkdAki96fBeok3V9lChoBmgJaA9DCNoB1xUzKHFAlIaUUpRoFU0GAWgWR0CSMKfK6nR+dX2UKGgGaAloD0MI7YMsC+YFc0CUhpRSlGgVTTgBaBZHQJIw8w1zhgp1fZQoaAZoCWgPQwgHJ6Jf20xwQJSGlFKUaBVNEAFoFkdAkjHQmE4//3V9lChoBmgJaA9DCE637BD/fnFAlIaUUpRoFUvyaBZHQJIzAS39aU11fZQoaAZoCWgPQwhm2v6VVQ1xQJSGlFKUaBVNEAFoFkdAkjMVnuiN83V9lChoBmgJaA9DCIOluoCXPm9AlIaUUpRoFUvyaBZHQJIzbQC0WuZ1fZQoaAZoCWgPQwh5Xb9gN0ZuQJSGlFKUaBVNEgFoFkdAkjQEypJf6XV9lChoBmgJaA9DCHGS5o9pam5AlIaUUpRoFU0GAWgWR0CSNFbC79Q5dWUu"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c094aa7e40bf309c918b353c15a4a7e318185c5c358d8853b07d4d074787a318
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b69142344536701bdedd2a2c7dc2c7a756920463932903f0f249e91777f4cde
+size 43393

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 263.3041167337582, "std_reward": 12.339890070660196, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-21T12:23:09.388526"}