first try ppo lunarlander

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 272.63 +/- 20.66
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **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:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7ff2ab9fe0e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff2ab9fe170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff2ab9fe200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff2ab9fe290>", "_build": "<function ActorCriticPolicy._build at 0x7ff2ab9fe320>", "forward": "<function ActorCriticPolicy.forward at 0x7ff2ab9fe3b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff2ab9fe440>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff2ab9fe4d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff2ab9fe560>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff2ab9fe5f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff2ab9fe680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff2aba4a7b0>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653642425.7210376, "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_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:": "gASVLBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAb7bvDGhcUCUhpRSlIwBbJRL2IwBdJRHQKkVuqVhTfl1fZQoaAZoCWgPQwjmyTUFsgJyQJSGlFKUaBVLyWgWR0CpFeDg62fDdX2UKGgGaAloD0MIsDxIT1FgcECUhpRSlGgVS+BoFkdAqRYB4Uvf0nV9lChoBmgJaA9DCGYtBaR9EnFAlIaUUpRoFUu9aBZHQKkWP7el9Bt1fZQoaAZoCWgPQwgqcoi4ObZxQJSGlFKUaBVL2WgWR0CpFmcbzbvgdX2UKGgGaAloD0MICVBTy1ZHckCUhpRSlGgVS8toFkdAqRZwdU83dnV9lChoBmgJaA9DCJRMTu3MjXFAlIaUUpRoFUvcaBZHQKkWiYTCcgB1fZQoaAZoCWgPQwg486s5wOBvQJSGlFKUaBVL22gWR0CpFo7lRxcWdX2UKGgGaAloD0MIOWOYE7TRI0CUhpRSlGgVS4NoFkdAqRbxE8aGYnV9lChoBmgJaA9DCGNBYVDmKnFAlIaUUpRoFUu2aBZHQKkX1Z1V5rx1fZQoaAZoCWgPQwghPxu5LlFyQJSGlFKUaBVL+WgWR0CpF+liBoVVdX2UKGgGaAloD0MIe/fHexXIcECUhpRSlGgVTQgBaBZHQKkYHgNPP9l1fZQoaAZoCWgPQwhZiXlWkqRxQJSGlFKUaBVL32gWR0CpGF/UF0PpdX2UKGgGaAloD0MIGk0uxoB5cUCUhpRSlGgVS+doFkdAqRhxSpBHC3V9lChoBmgJaA9DCDrrU45Jbm5AlIaUUpRoFUvMaBZHQKkYy2eg+Ql1fZQoaAZoCWgPQwjRrkLKjx9wQJSGlFKUaBVL6mgWR0CpGNQc5sCUdX2UKGgGaAloD0MIvmvQl96acECUhpRSlGgVTQUBaBZHQKkY31fVqet1fZQoaAZoCWgPQwi2vkhoiwxxQJSGlFKUaBVLyWgWR0CpGVOxjawmdX2UKGgGaAloD0MIEEBqEyddcECUhpRSlGgVS9VoFkdAqRmCpFTef3V9lChoBmgJaA9DCCY6yyyCgXFAlIaUUpRoFUvoaBZHQKkZltSAH3V1fZQoaAZoCWgPQwhsCfmg5z5xQJSGlFKUaBVL7WgWR0CpGZvCl7+ldX2UKGgGaAloD0MI4ExMF+LjcECUhpRSlGgVS/1oFkdAqRmhH7P6bnV9lChoBmgJaA9DCOQxA5UxEHFAlIaUUpRoFUvDaBZHQKkaTiBoVVR1fZQoaAZoCWgPQwhwlLw6x61xQJSGlFKUaBVLwGgWR0CpGnGAkLQYdX2UKGgGaAloD0MI9zsUBbrIcUCUhpRSlGgVS+9oFkdAqRrrfvWpZXV9lChoBmgJaA9DCI+pu7KLom9AlIaUUpRoFUvDaBZHQKkbPB2OhkB1fZQoaAZoCWgPQwg5RrJHqMNyQJSGlFKUaBVL62gWR0CpGz62nbZfdX2UKGgGaAloD0MIJ2co7ri0cECUhpRSlGgVS/BoFkdAqRto2ycCo3V9lChoBmgJaA9DCHIz3ICPYXNAlIaUUpRoFUv5aBZHQKkcBjXnQpp1fZQoaAZoCWgPQwgttd5vNAtvQJSGlFKUaBVLz2gWR0CpHHRFRYRvdX2UKGgGaAloD0MIg4b+CW5cc0CUhpRSlGgVS9BoFkdAqRxzIJZ4fXV9lChoBmgJaA9DCEJ3SZwV329AlIaUUpRoFUvWaBZHQKkcdAvcrRV1fZQoaAZoCWgPQwikNJvHoSlzQJSGlFKUaBVL5mgWR0CpHM1hb4ahdX2UKGgGaAloD0MIQpjbvVxAcUCUhpRSlGgVS7NoFkdAqRzmRmseXHV9lChoBmgJaA9DCGuCqPsA3nBAlIaUUpRoFUveaBZHQKlK4okzGgl1fZQoaAZoCWgPQwjdlsgFZ7FvQJSGlFKUaBVLtWgWR0CpS105lvqDdX2UKGgGaAloD0MIL4oe+Bg6RkCUhpRSlGgVS4xoFkdAqUvSKBNEgHV9lChoBmgJaA9DCPX1fM1ydnBAlIaUUpRoFUvqaBZHQKlMHAN5MUR1fZQoaAZoCWgPQwihgy7hkCNxQJSGlFKUaBVLt2gWR0CpTBvg3tKJdX2UKGgGaAloD0MIBTbn4NlbcECUhpRSlGgVTRABaBZHQKlMbEKmbb11fZQoaAZoCWgPQwjeHoSAvMlxQJSGlFKUaBVNAQFoFkdAqUyC6z3RHHV9lChoBmgJaA9DCG7b96g/fWtAlIaUUpRoFUu7aBZHQKlMqjPfKp11fZQoaAZoCWgPQwh0XI3sShhtQJSGlFKUaBVNwQFoFkdAqU03l2eQMnV9lChoBmgJaA9DCDBK0F/ozXFAlIaUUpRoFUvfaBZHQKlNRq1PWQR1fZQoaAZoCWgPQwgdklooGVdwQJSGlFKUaBVLyWgWR0CpTXCJGe+VdX2UKGgGaAloD0MIHsakv1eDckCUhpRSlGgVS+NoFkdAqU3BODaoM3V9lChoBmgJaA9DCJgz2xV6tGxAlIaUUpRoFU2eAmgWR0CpTiyuZCv6dX2UKGgGaAloD0MIN+LJbubUcUCUhpRSlGgVS+xoFkdAqU7mOjqOcXV9lChoBmgJaA9DCPGAsikXK3BAlIaUUpRoFUvQaBZHQKlO5dMTN+t1fZQoaAZoCWgPQwj76qpAbXhwQJSGlFKUaBVL6GgWR0CpT/k+xGDudX2UKGgGaAloD0MI7mDEPsEMckCUhpRSlGgVS9RoFkdAqVADtkWhy3V9lChoBmgJaA9DCAVqMXgYhnFAlIaUUpRoFU0BAWgWR0CpUB8neBQOdX2UKGgGaAloD0MIbvse9dfuUUCUhpRSlGgVTegDaBZHQKlQGUahpQF1fZQoaAZoCWgPQwgoRwGioPNwQJSGlFKUaBVLzGgWR0CpUIywfQrudX2UKGgGaAloD0MIUYiAQ+i5cUCUhpRSlGgVS9VoFkdAqVDRsKsuF3V9lChoBmgJaA9DCGr11VWBenNAlIaUUpRoFU0kAWgWR0CpUNhSDRMOdX2UKGgGaAloD0MIRdjw9IpRcUCUhpRSlGgVS/loFkdAqVEnR7Z393V9lChoBmgJaA9DCB3jiosjUXFAlIaUUpRoFUvbaBZHQKlRL+sHSnd1fZQoaAZoCWgPQwhE2zF1F5BzQJSGlFKUaBVL4WgWR0CpUaA+IMz/dX2UKGgGaAloD0MIKUAUzNjnckCUhpRSlGgVTTsBaBZHQKlRn5uZThp1fZQoaAZoCWgPQwgjLZW3I39SQJSGlFKUaBVN6ANoFkdAqVG1G3F1jnV9lChoBmgJaA9DCCMShZb1NW9AlIaUUpRoFUvHaBZHQKlR3X9zfaZ1fZQoaAZoCWgPQwiR8/4/jhZxQJSGlFKUaBVLz2gWR0CpUfKZML4OdX2UKGgGaAloD0MIj46rkR0GckCUhpRSlGgVTY4BaBZHQKlSbBOYYzl1fZQoaAZoCWgPQwjS/gdY62xwQJSGlFKUaBVLymgWR0CpUsGIj4YadX2UKGgGaAloD0MIks8rnvo0cECUhpRSlGgVS9poFkdAqVLgTqSowXV9lChoBmgJaA9DCPd14JzR+3JAlIaUUpRoFUvjaBZHQKlS+HuZ1FJ1fZQoaAZoCWgPQwh0JJf/0ENwQJSGlFKUaBVLwGgWR0CpUwek56t1dX2UKGgGaAloD0MIcH1Yb1SEckCUhpRSlGgVS/NoFkdAqVM/pIMBqHV9lChoBmgJaA9DCHH/kelQ73FAlIaUUpRoFUvUaBZHQKlTgS4e9zx1fZQoaAZoCWgPQwiqZWt9kSlyQJSGlFKUaBVL7GgWR0CpU9Fm4AjqdX2UKGgGaAloD0MIl299WC9Ec0CUhpRSlGgVS9loFkdAqVPXpOerdXV9lChoBmgJaA9DCLnCu1zE3HNAlIaUUpRoFUvuaBZHQKlUHFGXokl1fZQoaAZoCWgPQwgPfAxW3FVxQJSGlFKUaBVL2mgWR0CpVDn/1g6VdX2UKGgGaAloD0MII/PIH0w5cECUhpRSlGgVS+JoFkdAqVRWE25xznV9lChoBmgJaA9DCEfLgR5qnF9AlIaUUpRoFU3oA2gWR0CpVFuCf6GhdX2UKGgGaAloD0MIpics8UAMcUCUhpRSlGgVS9VoFkdAqVRqkZaV2XV9lChoBmgJaA9DCLnCu1wEAXNAlIaUUpRoFUvnaBZHQKlUq0a6z3R1fZQoaAZoCWgPQwiale1DntVxQJSGlFKUaBVLwWgWR0CpVLaR6nivdX2UKGgGaAloD0MI9WbUfBUkckCUhpRSlGgVS7JoFkdAqVUPiYLLIXV9lChoBmgJaA9DCEFn0qZqV3JAlIaUUpRoFUvPaBZHQKlVR1p0wJx1fZQoaAZoCWgPQwgY7IZtiwJCQJSGlFKUaBVLe2gWR0CpVXnYpUgkdX2UKGgGaAloD0MIu9IyUq+AcUCUhpRSlGgVS8VoFkdAqVWB95QgtHV9lChoBmgJaA9DCBEdAkfC63BAlIaUUpRoFUv7aBZHQKlVs1mapgl1fZQoaAZoCWgPQwjRHi+kQ4twQJSGlFKUaBVL5WgWR0CpViQob4rSdX2UKGgGaAloD0MI9fI7TaZ+cUCUhpRSlGgVS+RoFkdAqVZ5rJr+HnV9lChoBmgJaA9DCAhYq3bNVHNAlIaUUpRoFU0uAWgWR0CpVoyt3fQ8dX2UKGgGaAloD0MIIqXZPI48cUCUhpRSlGgVS+doFkdAqVaMYIjW1HV9lChoBmgJaA9DCACRfvs6Tm9AlIaUUpRoFUu+aBZHQKlWkncclw91fZQoaAZoCWgPQwhgIt46P6pxQJSGlFKUaBVL1GgWR0CpVuXjENvwdX2UKGgGaAloD0MIkQn4NRLtZUCUhpRSlGgVTbQBaBZHQKlW3eVs1sN1fZQoaAZoCWgPQwj0+SgjLnJvQJSGlFKUaBVL5mgWR0CpVu5AQg9vdX2UKGgGaAloD0MIz0nvG19ycUCUhpRSlGgVS+VoFkdAqVcKH/Lkj3V9lChoBmgJaA9DCKhV9IemD3BAlIaUUpRoFUvIaBZHQKlXC6uGKyh1fZQoaAZoCWgPQwiIuaRqu/ZyQJSGlFKUaBVL7GgWR0CpV3gr6LwXdX2UKGgGaAloD0MIiPNwAhPDcUCUhpRSlGgVS8JoFkdAqVeQNG3F1nV9lChoBmgJaA9DCM0Ew7mGdXBAlIaUUpRoFUvEaBZHQKlXwcz67/Z1fZQoaAZoCWgPQwgGZoUiXX1yQJSGlFKUaBVLyWgWR0CpWAEona37dX2UKGgGaAloD0MI5WA2AYZHc0CUhpRSlGgVS9toFkdAqVgIY1pCbHV9lChoBmgJaA9DCCocQSoFhHFAlIaUUpRoFUuwaBZHQKlYdKkl/pd1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 620, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.95, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 20, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

lunar_ppo_v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73750f241919e1a7611c316fd24553e40290dc5c442c67d229017ae702e543f8
+size 144109

lunar_ppo_v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

lunar_ppo_v2/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7ff2ab9fe0e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff2ab9fe170>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff2ab9fe200>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff2ab9fe290>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff2ab9fe320>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff2ab9fe3b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff2ab9fe440>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff2ab9fe4d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff2ab9fe560>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff2ab9fe5f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff2ab9fe680>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff2aba4a7b0>"
+    },
+    "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1653642425.7210376,
+    "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
+    },
+    "_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:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 620,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 20,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

lunar_ppo_v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:399ea85769530a5bb775b4337627689552edb1df629f050a0fda38e222ab6d66
+size 84893

lunar_ppo_v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58ae3cde2457855cb6631a094a0f92c2bc208180c15b89aea76130eea932fdf9
+size 43201

lunar_ppo_v2/pytorch_variables.pth

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

lunar_ppo_v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f49e9d6f654f81feae700814477cdcb8035cac791e499a7195a51e0b6d8d7ded
+size 203925

results.json

@@ -0,0 +1 @@
+{"mean_reward": 272.63179430929074, "std_reward": 20.65581698990019, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-27T09:26:40.842257"}