Upload LunarLander-v2 trained model using PPO MlpPolicy

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: 252.66 +/- 12.61
+      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 0x7f78df71a160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78df71a1f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78df71a280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78df71a310>", "_build": "<function ActorCriticPolicy._build at 0x7f78df71a3a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f78df71a430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f78df71a4c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78df71a550>", "_predict": "<function ActorCriticPolicy._predict at 0x7f78df71a5e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78df71a670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78df71a700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78df71a790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f78df714870>"}, "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": 1675699816989503557, "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:": "gAWVgRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIS1ewjXguZECUhpRSlIwBbJRN6AOMAXSUR0CcZ2Fn7HhkdX2UKGgGaAloD0MI2h8ot+0KX0CUhpRSlGgVTegDaBZHQJxqq2UjcEh1fZQoaAZoCWgPQwjNyYtMQJlhQJSGlFKUaBVN6ANoFkdAnHD+CPIXCXV9lChoBmgJaA9DCC/9S1IZp2xAlIaUUpRoFU2hAmgWR0CcdDinYQJ5dX2UKGgGaAloD0MIOq3boHbUZECUhpRSlGgVTegDaBZHQJx1VhWo3rF1fZQoaAZoCWgPQwgR4zWv6mRhQJSGlFKUaBVN6ANoFkdAnHqJ3X7LuHV9lChoBmgJaA9DCKIOK9zyW2BAlIaUUpRoFU3oA2gWR0Cce7uLJjlQdX2UKGgGaAloD0MIjrCoiNP7WECUhpRSlGgVTegDaBZHQJyCld6cAip1fZQoaAZoCWgPQwjDnQsjvU1iQJSGlFKUaBVN6ANoFkdAnIMWce8wpXV9lChoBmgJaA9DCH9QFymU911AlIaUUpRoFU3oA2gWR0CchexubZvldX2UKGgGaAloD0MIsYuiBz6YZUCUhpRSlGgVTegDaBZHQJyG+7xusLh1fZQoaAZoCWgPQwgUI0vm2LNlQJSGlFKUaBVN6ANoFkdAnIcMstkFwHV9lChoBmgJaA9DCIsZ4e1BhmBAlIaUUpRoFU3oA2gWR0CciBjlxOtXdX2UKGgGaAloD0MIFeP8TSheYkCUhpRSlGgVTegDaBZHQJyOAHpr1ul1fZQoaAZoCWgPQwhIUz2Zf+BcQJSGlFKUaBVN6ANoFkdAnJSMcU/OdHV9lChoBmgJaA9DCCL99nXg1GFAlIaUUpRoFU3oA2gWR0CcuaVR1oxpdX2UKGgGaAloD0MILPNWXYfYX0CUhpRSlGgVTegDaBZHQJy9OagElmh1fZQoaAZoCWgPQwicMcwJ2vVfQJSGlFKUaBVN6ANoFkdAnMCG1IAfdXV9lChoBmgJaA9DCHrCEg+o+2ZAlIaUUpRoFU3oA2gWR0CcyAVIqbz9dX2UKGgGaAloD0MI5xcl6K90YkCUhpRSlGgVTegDaBZHQJzMtfdAPd51fZQoaAZoCWgPQwgtlbcjHMhiQJSGlFKUaBVN6ANoFkdAnM519F4LTnV9lChoBmgJaA9DCAW/DTFeUmNAlIaUUpRoFU3oA2gWR0Cc1gHBUJfIdX2UKGgGaAloD0MIzv5Aue1gYUCUhpRSlGgVTegDaBZHQJzXXko4MnZ1fZQoaAZoCWgPQwiTb7a5MX5ZQJSGlFKUaBVN6ANoFkdAnN4GAwwj+3V9lChoBmgJaA9DCMoXtJCAzlhAlIaUUpRoFU3oA2gWR0Cc3oc9GI9DdX2UKGgGaAloD0MIeLRxxNoYYECUhpRSlGgVTegDaBZHQJzhiy/sVtZ1fZQoaAZoCWgPQwho5zQLtAhiQJSGlFKUaBVN6ANoFkdAnOKigCfYjHV9lChoBmgJaA9DCJSHhVpTDWJAlIaUUpRoFU3oA2gWR0Cc4rP+XJHRdX2UKGgGaAloD0MItoXnpWIOZUCUhpRSlGgVTegDaBZHQJzjwGHHmzV1fZQoaAZoCWgPQwgUX+0ozntjQJSGlFKUaBVN6ANoFkdAnOf8r3CbdHV9lChoBmgJaA9DCD9SRIZVnWBAlIaUUpRoFU3oA2gWR0Cc7IqEeyRkdX2UKGgGaAloD0MIww/Op455MkCUhpRSlGgVTTABaBZHQJzzm8274BV1fZQoaAZoCWgPQwhIwr6dxJdlQJSGlFKUaBVN6ANoFkdAnRRAbIcR2HV9lChoBmgJaA9DCIwubw7Xk11AlIaUUpRoFU3oA2gWR0CdF1uYhMakdX2UKGgGaAloD0MIOL2L9+PzYUCUhpRSlGgVTegDaBZHQJ0agT/Q0Gh1fZQoaAZoCWgPQwj1LAjlfe1jQJSGlFKUaBVN6ANoFkdAnSDgx33Yc3V9lChoBmgJaA9DCNY3MLlR+F9AlIaUUpRoFU3oA2gWR0CdJBhJAdGRdX2UKGgGaAloD0MIPx2PGSjlYECUhpRSlGgVTegDaBZHQJ0lW78Nx2l1fZQoaAZoCWgPQwiHNZVF4WhuQJSGlFKUaBVNPQFoFkdAnSjry6MBIXV9lChoBmgJaA9DCDOHpBbKkWFAlIaUUpRoFU3oA2gWR0CdKt14Pf8/dX2UKGgGaAloD0MIkQw5th6vZkCUhpRSlGgVTegDaBZHQJ0sR0fYBeZ1fZQoaAZoCWgPQwiRtYZSe89iQJSGlFKUaBVN6ANoFkdAnTMnRCx/u3V9lChoBmgJaA9DCLJMv0S8cVxAlIaUUpRoFU3oA2gWR0CdM6kXUH6edX2UKGgGaAloD0MIseHplfI8cECUhpRSlGgVTbADaBZHQJ00eCz1K5F1fZQoaAZoCWgPQwiDv1/MlqxiQJSGlFKUaBVN6ANoFkdAnTan2h7E53V9lChoBmgJaA9DCJKU9DA0YGJAlIaUUpRoFU3oA2gWR0CdORWTot+TdX2UKGgGaAloD0MIpDhHHZ0/Y0CUhpRSlGgVTegDaBZHQJ0+9G9YfXB1fZQoaAZoCWgPQwgjEK/rl1VuQJSGlFKUaBVNgAJoFkdAnUQY1gpjMHV9lChoBmgJaA9DCHL75ZMVFV9AlIaUUpRoFU3oA2gWR0CdRW8IRh+fdX2UKGgGaAloD0MIzzC1pQ6DW0CUhpRSlGgVTegDaBZHQJ1NxDBuXNV1fZQoaAZoCWgPQwijdVQ1QbZdQJSGlFKUaBVN6ANoFkdAnWuZwS8J2XV9lChoBmgJaA9DCNcv2A3bJmZAlIaUUpRoFU3oA2gWR0CddZwjMV1wdX2UKGgGaAloD0MIAfbRqasKYUCUhpRSlGgVTegDaBZHQJ16Nf+jua51fZQoaAZoCWgPQwgYz6Ch/5VgQJSGlFKUaBVN6ANoFkdAnXwB0ZFXrHV9lChoBmgJaA9DCOygEtfxfXBAlIaUUpRoFU3UAmgWR0CdfHNTLns+dX2UKGgGaAloD0MIgO7Lme0GYkCUhpRSlGgVTegDaBZHQJ2A/1h9b5d1fZQoaAZoCWgPQwhG0JhJ1HZeQJSGlFKUaBVN6ANoFkdAnYO0a/ATI3V9lChoBmgJaA9DCLyS5Lm+2GRAlIaUUpRoFU3oA2gWR0CdhWXVLBbfdX2UKGgGaAloD0MIrprniPzDYUCUhpRSlGgVTegDaBZHQJ2MBBF/hEV1fZQoaAZoCWgPQwh7o1aYPsFgQJSGlFKUaBVN6ANoFkdAnYx6FZgXuXV9lChoBmgJaA9DCAhYq3ZNVGVAlIaUUpRoFU3oA2gWR0CdjSSt/4IsdX2UKGgGaAloD0MI8WPMXUvCb0CUhpRSlGgVTTcDaBZHQJ2PeF36hxp1fZQoaAZoCWgPQwgcKVskbbpmQJSGlFKUaBVN6ANoFkdAnZC/bsWweXV9lChoBmgJaA9DCNrLttNWDWJAlIaUUpRoFU3oA2gWR0CdlPb212JSdX2UKGgGaAloD0MIjDBFubQuZECUhpRSlGgVTegDaBZHQJ2Zqp5u63B1fZQoaAZoCWgPQwgMzXUa6cZkQJSGlFKUaBVN6ANoFkdAnaEYMOPNmnV9lChoBmgJaA9DCMi2DDhLiFtAlIaUUpRoFU3oA2gWR0CdxmwUg0TDdX2UKGgGaAloD0MIglg2c8gMZUCUhpRSlGgVTegDaBZHQJ3Q+gVXV9Z1fZQoaAZoCWgPQwgrS3SW2UNiQJSGlFKUaBVN6ANoFkdAndSoSlFc6nV9lChoBmgJaA9DCLZJRWPt22FAlIaUUpRoFU3oA2gWR0Cd1feIEbHZdX2UKGgGaAloD0MItFn1uVoLYkCUhpRSlGgVTegDaBZHQJ3WRGI9C/p1fZQoaAZoCWgPQwhNEeD0LuRiQJSGlFKUaBVN6ANoFkdAndkyXY150XV9lChoBmgJaA9DCJxQiIBDQ2BAlIaUUpRoFU3oA2gWR0Cd2uU1Q66rdX2UKGgGaAloD0MIMJsAw/JTZUCUhpRSlGgVTegDaBZHQJ3b6ebutwJ1fZQoaAZoCWgPQwjzIagaPdRiQJSGlFKUaBVN6ANoFkdAneF/73wkPnV9lChoBmgJaA9DCLO1vkho3l9AlIaUUpRoFU3oA2gWR0Cd4ef1YhdMdX2UKGgGaAloD0MIZHPVPEe5V0CUhpRSlGgVTegDaBZHQJ3ijY+Sr5t1fZQoaAZoCWgPQwgAVdy4RXxkQJSGlFKUaBVN6ANoFkdAneUeSwGGEnV9lChoBmgJaA9DCG/x8J4DmmFAlIaUUpRoFU3oA2gWR0Cd5opM6BAfdX2UKGgGaAloD0MIDTm2niGYYkCUhpRSlGgVTegDaBZHQJ3sJn8Kohp1fZQoaAZoCWgPQwgbnl4py29iQJSGlFKUaBVN6ANoFkdAnfK1yR0U5HV9lChoBmgJaA9DCG/VdaimiDxAlIaUUpRoFU0cAWgWR0Cd+nbtqpLmdX2UKGgGaAloD0MIvxBy3v9WYUCUhpRSlGgVTegDaBZHQJ38StW+49Z1fZQoaAZoCWgPQwj4bB0cbENtQJSGlFKUaBVNggFoFkdAnf0f+OwPiHV9lChoBmgJaA9DCNNsHodBv2NAlIaUUpRoFU3oA2gWR0CeGhSMLncMdX2UKGgGaAloD0MIeEfGavMdYECUhpRSlGgVTegDaBZHQJ4kUTdtVJd1fZQoaAZoCWgPQwiBJVexeNxhQJSGlFKUaBVN6ANoFkdAnilmVJL/THV9lChoBmgJaA9DCIOFkzR/VFxAlIaUUpRoFU3oA2gWR0CeK5M5OrQxdX2UKGgGaAloD0MIirDh6ZXZXkCUhpRSlGgVTegDaBZHQJ4sCicoYvZ1fZQoaAZoCWgPQwgWwf9WMtRiQJSGlFKUaBVN6ANoFkdAnjCLrTpgTnV9lChoBmgJaA9DCDfjNESVCGNAlIaUUpRoFU3oA2gWR0CeM0grH2h7dX2UKGgGaAloD0MIM9/BT5x0Y0CUhpRSlGgVTegDaBZHQJ408jyFwkx1fZQoaAZoCWgPQwgiiskb4J5jQJSGlFKUaBVN6ANoFkdAnjuEiILw4XV9lChoBmgJaA9DCHb6QV2ke2VAlIaUUpRoFU3oA2gWR0CePK5qdpZfdX2UKGgGaAloD0MIFobI6evqYkCUhpRSlGgVTegDaBZHQJ5BB9y925h1fZQoaAZoCWgPQwjcD3hgAIleQJSGlFKUaBVN6ANoFkdAnkX3ko4MnnV9lChoBmgJaA9DCKd38X7cdltAlIaUUpRoFU3oA2gWR0CeSxewcHW0dX2UKGgGaAloD0MImGn7V9ZMZUCUhpRSlGgVTegDaBZHQJ5Q7Ks+3Yt1fZQoaAZoCWgPQwjcnbXbLnhiQJSGlFKUaBVN6ANoFkdAnlJ7Qswta3V9lChoBmgJaA9DCN0lcVZEQlxAlIaUUpRoFU3oA2gWR0CeU1aZx7zDdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 252, "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "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 sha256:043501d3a23a10f0e84b8c5da944221efd3c4c5035ecea1fc05186b70e80df7c
+size 147552

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 0x7f78df71a160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78df71a1f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78df71a280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78df71a310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f78df71a3a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f78df71a430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f78df71a4c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78df71a550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f78df71a5e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78df71a670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78df71a700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78df71a790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f78df714870>"
+    },
+    "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": 1675699816989503557,
+    "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:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 252,
+    "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 sha256:7cd55d6eeccab23608d193eae4aeb8ce3b8ccbe4022d472bab3dc430945f607d
+size 88057

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:429d99a7e6e9d9ded993b5a4d4dedf8f8f8e1dc66a0ec612875d98b5563d2b96
+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": 252.6638548741934, "std_reward": 12.613151809511917, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-06T17:10:38.336697"}