First commit

.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 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 283.49 +/- 13.74
+      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
+  

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 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 0x7fb3f7c64cb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb3f7c64d40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb3f7c64dd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb3f7c64e60>", "_build": "<function ActorCriticPolicy._build at 0x7fb3f7c64ef0>", "forward": "<function ActorCriticPolicy.forward at 0x7fb3f7c64f80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb3f7c6b050>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb3f7c6b0e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb3f7c6b170>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb3f7c6b200>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb3f7c6b290>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb3f7cb1b40>"}, "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": 4000000, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651700971.1332002, "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": 0.2004608, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIPXyZKIJIcECUhpRSlIwBbJRL6YwBdJRHQJkmIPuogmt1fZQoaAZoCWgPQwgabOo8agBwQJSGlFKUaBVL2WgWR0CZJnuctoSMdX2UKGgGaAloD0MIjGSPULMBckCUhpRSlGgVS7poFkdAmSc7xiG34XV9lChoBmgJaA9DCPRRRlwAU3BAlIaUUpRoFUvWaBZHQJknQ4o7V8V1fZQoaAZoCWgPQwjjM9k/T41yQJSGlFKUaBVL9WgWR0CZJ1qdH2AYdX2UKGgGaAloD0MIS+guifOpckCUhpRSlGgVS8toFkdAmSeHEyckMXV9lChoBmgJaA9DCAbWcfxQ/HBAlIaUUpRoFUvKaBZHQJknp8gIQe51fZQoaAZoCWgPQwikchO1NARxQJSGlFKUaBVLxWgWR0CZJ7nSfDk3dX2UKGgGaAloD0MI/zwNGCSRVkCUhpRSlGgVS9FoFkdAmSfAyRB/qnV9lChoBmgJaA9DCA9G7BPAxHJAlIaUUpRoFUvLaBZHQJkoEejmCAd1fZQoaAZoCWgPQwgSM/s8RhZzQJSGlFKUaBVL+2gWR0CZKFS13MY/dX2UKGgGaAloD0MIavXVVUHDcUCUhpRSlGgVS99oFkdAmShYcBEKE3V9lChoBmgJaA9DCF8mipB6xXJAlIaUUpRoFUvHaBZHQJkoYxgy/K11fZQoaAZoCWgPQwjiAWVT7hlyQJSGlFKUaBVLx2gWR0CZKGpBHCoCdX2UKGgGaAloD0MIrp6T3vcvcECUhpRSlGgVS8poFkdAmSjlSsKb8XV9lChoBmgJaA9DCFevIqPDGXJAlIaUUpRoFUvYaBZHQJkpc8cMmWt1fZQoaAZoCWgPQwjnGJC9XkRwQJSGlFKUaBVLyGgWR0CZKgcvM8oydX2UKGgGaAloD0MISU27mGYDcUCUhpRSlGgVS7NoFkdAmSofMwDeTHV9lChoBmgJaA9DCE637BC/tXBAlIaUUpRoFUu7aBZHQJkxIByS3b51fZQoaAZoCWgPQwguAmN9A3puQJSGlFKUaBVLxWgWR0CZMU+8Gs3idX2UKGgGaAloD0MI4+Ko3ETncUCUhpRSlGgVS9toFkdAmTFniR4hU3V9lChoBmgJaA9DCLu4jQawP3FAlIaUUpRoFUvpaBZHQJkxb9MsYl91fZQoaAZoCWgPQwiOrPwy2PlxQJSGlFKUaBVL/2gWR0CZMaLfk3judX2UKGgGaAloD0MIvCTOiqhpcECUhpRSlGgVS8loFkdAmTG8RYigTXV9lChoBmgJaA9DCNGvrZ8+vXBAlIaUUpRoFUvBaBZHQJkx52OhkAh1fZQoaAZoCWgPQwjUfJV87JVzQJSGlFKUaBVLy2gWR0CZMh8xsVL0dX2UKGgGaAloD0MIJy8yAX8YckCUhpRSlGgVS9VoFkdAmTIuGwiaAnV9lChoBmgJaA9DCOp29pUHdnRAlIaUUpRoFUvaaBZHQJkyTYChew91fZQoaAZoCWgPQwiA1vz4izpwQJSGlFKUaBVL2mgWR0CZMtl7MPjGdX2UKGgGaAloD0MIGT230FWucECUhpRSlGgVS9ZoFkdAmTNfdyksSXV9lChoBmgJaA9DCNMyUu8pgWJAlIaUUpRoFU3oA2gWR0CZM2hH9WIXdX2UKGgGaAloD0MIkbkyqDbfckCUhpRSlGgVS8xoFkdAmTPUIHC40HV9lChoBmgJaA9DCN3vUBRoYHBAlIaUUpRoFUu8aBZHQJkz8/W1+iJ1fZQoaAZoCWgPQwhBEYsYtlFwQJSGlFKUaBVLv2gWR0CZNBrqdH2AdX2UKGgGaAloD0MIBU62gXuQckCUhpRSlGgVS+5oFkdAmTQ2oegctHV9lChoBmgJaA9DCJa04hsKL1RAlIaUUpRoFUucaBZHQJk0Vc1O0sx1fZQoaAZoCWgPQwh81cqEH61xQJSGlFKUaBVL5mgWR0CZNF2USqVAdX2UKGgGaAloD0MIqKePwN+kcUCUhpRSlGgVS8poFkdAmTSHBLwnY3V9lChoBmgJaA9DCIlCy7r/L25AlIaUUpRoFUvhaBZHQJk0h4NZvDR1fZQoaAZoCWgPQwh0JQLVP2NzQJSGlFKUaBVLvGgWR0CZNNmMfigkdX2UKGgGaAloD0MIoE55dOO2cUCUhpRSlGgVS+loFkdAmTUSVGCqZXV9lChoBmgJaA9DCG9kHvmD7XJAlIaUUpRoFU0IAWgWR0CZNTreZXuFdX2UKGgGaAloD0MIj8L1KNz+ZkCUhpRSlGgVTegDaBZHQJk1PoB7u2J1fZQoaAZoCWgPQwj+KsB3WzdwQJSGlFKUaBVLz2gWR0CZNYqlgtvodX2UKGgGaAloD0MIFcrC19drc0CUhpRSlGgVTQkBaBZHQJk1mSDAaeh1fZQoaAZoCWgPQwgbaD7nruNwQJSGlFKUaBVLwGgWR0CZNcOkcjqwdX2UKGgGaAloD0MIdnCwN7H/bUCUhpRSlGgVS8xoFkdAmTXqxPfsNXV9lChoBmgJaA9DCFR0JJf/B29AlIaUUpRoFUu6aBZHQJk2Zdv863l1fZQoaAZoCWgPQwhTdvpBXXJwQJSGlFKUaBVLtWgWR0CZNnnuy/sWdX2UKGgGaAloD0MI1/uNdlx0ckCUhpRSlGgVS99oFkdAmTafW+XZ5HV9lChoBmgJaA9DCCJuTiXDgHNAlIaUUpRoFUvvaBZHQJk2tXr+o991fZQoaAZoCWgPQwi/RLx1vqhwQJSGlFKUaBVLymgWR0CZNuSDh99ddX2UKGgGaAloD0MIOKClK5iOcECUhpRSlGgVS8xoFkdAmTbqk2xY73V9lChoBmgJaA9DCLnF/NwQkXBAlIaUUpRoFUutaBZHQJk3NxBE8aJ1fZQoaAZoCWgPQwhIUWfuYXtzQJSGlFKUaBVL9GgWR0CZNzqQiiZfdX2UKGgGaAloD0MI1ZP5R9+CcUCUhpRSlGgVS81oFkdAmTdCFfzBh3V9lChoBmgJaA9DCB3mywswyW1AlIaUUpRoFUu7aBZHQJk3ZsANoal1fZQoaAZoCWgPQwi6vDlca6hxQJSGlFKUaBVL2mgWR0CZN5zwtrbhdX2UKGgGaAloD0MIfHxCdp6AcECUhpRSlGgVS8NoFkdAmTfWmk30gHV9lChoBmgJaA9DCDaxwFf0oXJAlIaUUpRoFUvFaBZHQJk37RSgoPV1fZQoaAZoCWgPQwghdTv7ig5zQJSGlFKUaBVL6mgWR0CZOJcvduYQdX2UKGgGaAloD0MIdQZGXpYbcUCUhpRSlGgVS+FoFkdAmTiopH7P6nV9lChoBmgJaA9DCDTaqiQyVHJAlIaUUpRoFUu2aBZHQJk4+W/rSmZ1fZQoaAZoCWgPQwhMpDSbxwhyQJSGlFKUaBVLr2gWR0CZORy/9Hc2dX2UKGgGaAloD0MI76gxIebVcUCUhpRSlGgVS+RoFkdAmTlEsOG0u3V9lChoBmgJaA9DCK+Xpghw/FRAlIaUUpRoFUuJaBZHQJk5X9jwx351fZQoaAZoCWgPQwjKiuHqAKlwQJSGlFKUaBVL6mgWR0CZOW4EwFkhdX2UKGgGaAloD0MI8N5RY4JYcECUhpRSlGgVS8loFkdAmTlxJul41XV9lChoBmgJaA9DCNz0Zz9SlXJAlIaUUpRoFUu5aBZHQJk5k384xUN1fZQoaAZoCWgPQwjU0twKIeZyQJSGlFKUaBVL/mgWR0CZOdMCLdeqdX2UKGgGaAloD0MIAK358RdQb0CUhpRSlGgVS9NoFkdAmToUnCwbEXV9lChoBmgJaA9DCDqxh/Zx5nJAlIaUUpRoFUviaBZHQJk6H1bqyGB1fZQoaAZoCWgPQwjcgTrl0Q1NQJSGlFKUaBVLuGgWR0CZOjVSXMQmdX2UKGgGaAloD0MInN8w0eDecUCUhpRSlGgVS/JoFkdAmTsQAp8WsXV9lChoBmgJaA9DCPM4DOZvE3NAlIaUUpRoFUu7aBZHQJk7ckleF+N1fZQoaAZoCWgPQwh6Nqs+V4pwQJSGlFKUaBVL1mgWR0CZO31He7+UdX2UKGgGaAloD0MIbqKW5pZAcUCUhpRSlGgVS7doFkdAmTuIaxX4kHV9lChoBmgJaA9DCEbu6epOQHRAlIaUUpRoFUvjaBZHQJk7mnuRcNZ1fZQoaAZoCWgPQwjLgok/CoFzQJSGlFKUaBVLvmgWR0CZO/N/e+EidX2UKGgGaAloD0MIpIriVRYPckCUhpRSlGgVS9NoFkdAmTwThDPWx3V9lChoBmgJaA9DCGA6rdvgynBAlIaUUpRoFUvZaBZHQJk8f80k4WF1fZQoaAZoCWgPQwjXaaSlMtNxQJSGlFKUaBVL+GgWR0CZPLgqVhTgdX2UKGgGaAloD0MIFvw2xHjgckCUhpRSlGgVTQIBaBZHQJk8780k4WF1fZQoaAZoCWgPQwgHKA01ChlzQJSGlFKUaBVL62gWR0CZPQ2itaIOdX2UKGgGaAloD0MIDI/9LJbVcECUhpRSlGgVS9hoFkdAmT0Re1KGtnV9lChoBmgJaA9DCO/hkuPOMnJAlIaUUpRoFUvmaBZHQJk9S6K+BYp1fZQoaAZoCWgPQwhgHccPVTZyQJSGlFKUaBVL32gWR0CZPUvLX+VDdX2UKGgGaAloD0MIGlJF8apBcUCUhpRSlGgVS89oFkdAmT3vAj6eoXV9lChoBmgJaA9DCMxCO6fZ+HJAlIaUUpRoFUu9aBZHQJk+G0ngHeJ1fZQoaAZoCWgPQwgHtd/ayQtyQJSGlFKUaBVL12gWR0CZRWUx20RfdX2UKGgGaAloD0MI7j7HR0upc0CUhpRSlGgVS+FoFkdAmUV4REnb7HV9lChoBmgJaA9DCG4yqgxjM29AlIaUUpRoFUvIaBZHQJlFimtQsPJ1fZQoaAZoCWgPQwi8XS9N0fFxQJSGlFKUaBVLuWgWR0CZReAYpDu0dX2UKGgGaAloD0MIjGmme50ETECUhpRSlGgVS5loFkdAmUX2PDHfdnV9lChoBmgJaA9DCI6SV+cYoXJAlIaUUpRoFU0LAWgWR0CZRfojv/ipdX2UKGgGaAloD0MIjgWFQdlIcUCUhpRSlGgVS+BoFkdAmUYBR2r4nHV9lChoBmgJaA9DCF0WE5vPinNAlIaUUpRoFUvBaBZHQJlGW5paibl1fZQoaAZoCWgPQwh4mzdOCvxxQJSGlFKUaBVL0mgWR0CZRrMt9QXRdX2UKGgGaAloD0MIT8sPXOVFckCUhpRSlGgVS+toFkdAmUa2+bmU4nV9lChoBmgJaA9DCDSeCOK8HnFAlIaUUpRoFUvHaBZHQJlGyHaews51ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 976, "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-5.13.0-40-generic-x86_64-with-debian-bullseye-sid #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022", "Python": "3.7.10", "Stable-Baselines3": "1.5.1a5", "PyTorch": "1.11.0", "GPU Enabled": "False", "Numpy": "1.21.2", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11182a0fed31a3ba8233d2eec6fb7b71373950e090fe5bc5f9531bcd11fa228e
+size 143709

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.1a5

ppo-LunarLander-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 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 0x7fb3f7c64cb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb3f7c64d40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb3f7c64dd0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb3f7c64e60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb3f7c64ef0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb3f7c64f80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb3f7c6b050>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb3f7c6b0e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb3f7c6b170>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb3f7c6b200>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb3f7c6b290>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb3f7cb1b40>"
+    },
+    "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": 4000000,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1651700971.1332002,
+    "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": 0.2004608,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIPXyZKIJIcECUhpRSlIwBbJRL6YwBdJRHQJkmIPuogmt1fZQoaAZoCWgPQwgabOo8agBwQJSGlFKUaBVL2WgWR0CZJnuctoSMdX2UKGgGaAloD0MIjGSPULMBckCUhpRSlGgVS7poFkdAmSc7xiG34XV9lChoBmgJaA9DCPRRRlwAU3BAlIaUUpRoFUvWaBZHQJknQ4o7V8V1fZQoaAZoCWgPQwjjM9k/T41yQJSGlFKUaBVL9WgWR0CZJ1qdH2AYdX2UKGgGaAloD0MIS+guifOpckCUhpRSlGgVS8toFkdAmSeHEyckMXV9lChoBmgJaA9DCAbWcfxQ/HBAlIaUUpRoFUvKaBZHQJknp8gIQe51fZQoaAZoCWgPQwikchO1NARxQJSGlFKUaBVLxWgWR0CZJ7nSfDk3dX2UKGgGaAloD0MI/zwNGCSRVkCUhpRSlGgVS9FoFkdAmSfAyRB/qnV9lChoBmgJaA9DCA9G7BPAxHJAlIaUUpRoFUvLaBZHQJkoEejmCAd1fZQoaAZoCWgPQwgSM/s8RhZzQJSGlFKUaBVL+2gWR0CZKFS13MY/dX2UKGgGaAloD0MIavXVVUHDcUCUhpRSlGgVS99oFkdAmShYcBEKE3V9lChoBmgJaA9DCF8mipB6xXJAlIaUUpRoFUvHaBZHQJkoYxgy/K11fZQoaAZoCWgPQwjiAWVT7hlyQJSGlFKUaBVLx2gWR0CZKGpBHCoCdX2UKGgGaAloD0MIrp6T3vcvcECUhpRSlGgVS8poFkdAmSjlSsKb8XV9lChoBmgJaA9DCFevIqPDGXJAlIaUUpRoFUvYaBZHQJkpc8cMmWt1fZQoaAZoCWgPQwjnGJC9XkRwQJSGlFKUaBVLyGgWR0CZKgcvM8oydX2UKGgGaAloD0MISU27mGYDcUCUhpRSlGgVS7NoFkdAmSofMwDeTHV9lChoBmgJaA9DCE637BC/tXBAlIaUUpRoFUu7aBZHQJkxIByS3b51fZQoaAZoCWgPQwguAmN9A3puQJSGlFKUaBVLxWgWR0CZMU+8Gs3idX2UKGgGaAloD0MI4+Ko3ETncUCUhpRSlGgVS9toFkdAmTFniR4hU3V9lChoBmgJaA9DCLu4jQawP3FAlIaUUpRoFUvpaBZHQJkxb9MsYl91fZQoaAZoCWgPQwiOrPwy2PlxQJSGlFKUaBVL/2gWR0CZMaLfk3judX2UKGgGaAloD0MIvCTOiqhpcECUhpRSlGgVS8loFkdAmTG8RYigTXV9lChoBmgJaA9DCNGvrZ8+vXBAlIaUUpRoFUvBaBZHQJkx52OhkAh1fZQoaAZoCWgPQwjUfJV87JVzQJSGlFKUaBVLy2gWR0CZMh8xsVL0dX2UKGgGaAloD0MIJy8yAX8YckCUhpRSlGgVS9VoFkdAmTIuGwiaAnV9lChoBmgJaA9DCOp29pUHdnRAlIaUUpRoFUvaaBZHQJkyTYChew91fZQoaAZoCWgPQwiA1vz4izpwQJSGlFKUaBVL2mgWR0CZMtl7MPjGdX2UKGgGaAloD0MIGT230FWucECUhpRSlGgVS9ZoFkdAmTNfdyksSXV9lChoBmgJaA9DCNMyUu8pgWJAlIaUUpRoFU3oA2gWR0CZM2hH9WIXdX2UKGgGaAloD0MIkbkyqDbfckCUhpRSlGgVS8xoFkdAmTPUIHC40HV9lChoBmgJaA9DCN3vUBRoYHBAlIaUUpRoFUu8aBZHQJkz8/W1+iJ1fZQoaAZoCWgPQwhBEYsYtlFwQJSGlFKUaBVLv2gWR0CZNBrqdH2AdX2UKGgGaAloD0MIBU62gXuQckCUhpRSlGgVS+5oFkdAmTQ2oegctHV9lChoBmgJaA9DCJa04hsKL1RAlIaUUpRoFUucaBZHQJk0Vc1O0sx1fZQoaAZoCWgPQwh81cqEH61xQJSGlFKUaBVL5mgWR0CZNF2USqVAdX2UKGgGaAloD0MIqKePwN+kcUCUhpRSlGgVS8poFkdAmTSHBLwnY3V9lChoBmgJaA9DCIlCy7r/L25AlIaUUpRoFUvhaBZHQJk0h4NZvDR1fZQoaAZoCWgPQwh0JQLVP2NzQJSGlFKUaBVLvGgWR0CZNNmMfigkdX2UKGgGaAloD0MIoE55dOO2cUCUhpRSlGgVS+loFkdAmTUSVGCqZXV9lChoBmgJaA9DCG9kHvmD7XJAlIaUUpRoFU0IAWgWR0CZNTreZXuFdX2UKGgGaAloD0MIj8L1KNz+ZkCUhpRSlGgVTegDaBZHQJk1PoB7u2J1fZQoaAZoCWgPQwj+KsB3WzdwQJSGlFKUaBVLz2gWR0CZNYqlgtvodX2UKGgGaAloD0MIFcrC19drc0CUhpRSlGgVTQkBaBZHQJk1mSDAaeh1fZQoaAZoCWgPQwgbaD7nruNwQJSGlFKUaBVLwGgWR0CZNcOkcjqwdX2UKGgGaAloD0MIdnCwN7H/bUCUhpRSlGgVS8xoFkdAmTXqxPfsNXV9lChoBmgJaA9DCFR0JJf/B29AlIaUUpRoFUu6aBZHQJk2Zdv863l1fZQoaAZoCWgPQwhTdvpBXXJwQJSGlFKUaBVLtWgWR0CZNnnuy/sWdX2UKGgGaAloD0MI1/uNdlx0ckCUhpRSlGgVS99oFkdAmTafW+XZ5HV9lChoBmgJaA9DCCJuTiXDgHNAlIaUUpRoFUvvaBZHQJk2tXr+o991fZQoaAZoCWgPQwi/RLx1vqhwQJSGlFKUaBVLymgWR0CZNuSDh99ddX2UKGgGaAloD0MIOKClK5iOcECUhpRSlGgVS8xoFkdAmTbqk2xY73V9lChoBmgJaA9DCLnF/NwQkXBAlIaUUpRoFUutaBZHQJk3NxBE8aJ1fZQoaAZoCWgPQwhIUWfuYXtzQJSGlFKUaBVL9GgWR0CZNzqQiiZfdX2UKGgGaAloD0MI1ZP5R9+CcUCUhpRSlGgVS81oFkdAmTdCFfzBh3V9lChoBmgJaA9DCB3mywswyW1AlIaUUpRoFUu7aBZHQJk3ZsANoal1fZQoaAZoCWgPQwi6vDlca6hxQJSGlFKUaBVL2mgWR0CZN5zwtrbhdX2UKGgGaAloD0MIfHxCdp6AcECUhpRSlGgVS8NoFkdAmTfWmk30gHV9lChoBmgJaA9DCDaxwFf0oXJAlIaUUpRoFUvFaBZHQJk37RSgoPV1fZQoaAZoCWgPQwghdTv7ig5zQJSGlFKUaBVL6mgWR0CZOJcvduYQdX2UKGgGaAloD0MIdQZGXpYbcUCUhpRSlGgVS+FoFkdAmTiopH7P6nV9lChoBmgJaA9DCDTaqiQyVHJAlIaUUpRoFUu2aBZHQJk4+W/rSmZ1fZQoaAZoCWgPQwhMpDSbxwhyQJSGlFKUaBVLr2gWR0CZORy/9Hc2dX2UKGgGaAloD0MI76gxIebVcUCUhpRSlGgVS+RoFkdAmTlEsOG0u3V9lChoBmgJaA9DCK+Xpghw/FRAlIaUUpRoFUuJaBZHQJk5X9jwx351fZQoaAZoCWgPQwjKiuHqAKlwQJSGlFKUaBVL6mgWR0CZOW4EwFkhdX2UKGgGaAloD0MI8N5RY4JYcECUhpRSlGgVS8loFkdAmTlxJul41XV9lChoBmgJaA9DCNz0Zz9SlXJAlIaUUpRoFUu5aBZHQJk5k384xUN1fZQoaAZoCWgPQwjU0twKIeZyQJSGlFKUaBVL/mgWR0CZOdMCLdeqdX2UKGgGaAloD0MIAK358RdQb0CUhpRSlGgVS9NoFkdAmToUnCwbEXV9lChoBmgJaA9DCDqxh/Zx5nJAlIaUUpRoFUviaBZHQJk6H1bqyGB1fZQoaAZoCWgPQwjcgTrl0Q1NQJSGlFKUaBVLuGgWR0CZOjVSXMQmdX2UKGgGaAloD0MInN8w0eDecUCUhpRSlGgVS/JoFkdAmTsQAp8WsXV9lChoBmgJaA9DCPM4DOZvE3NAlIaUUpRoFUu7aBZHQJk7ckleF+N1fZQoaAZoCWgPQwh6Nqs+V4pwQJSGlFKUaBVL1mgWR0CZO31He7+UdX2UKGgGaAloD0MIbqKW5pZAcUCUhpRSlGgVS7doFkdAmTuIaxX4kHV9lChoBmgJaA9DCEbu6epOQHRAlIaUUpRoFUvjaBZHQJk7mnuRcNZ1fZQoaAZoCWgPQwjLgok/CoFzQJSGlFKUaBVLvmgWR0CZO/N/e+EidX2UKGgGaAloD0MIpIriVRYPckCUhpRSlGgVS9NoFkdAmTwThDPWx3V9lChoBmgJaA9DCGA6rdvgynBAlIaUUpRoFUvZaBZHQJk8f80k4WF1fZQoaAZoCWgPQwjXaaSlMtNxQJSGlFKUaBVL+GgWR0CZPLgqVhTgdX2UKGgGaAloD0MIFvw2xHjgckCUhpRSlGgVTQIBaBZHQJk8780k4WF1fZQoaAZoCWgPQwgHKA01ChlzQJSGlFKUaBVL62gWR0CZPQ2itaIOdX2UKGgGaAloD0MIDI/9LJbVcECUhpRSlGgVS9hoFkdAmT0Re1KGtnV9lChoBmgJaA9DCO/hkuPOMnJAlIaUUpRoFUvmaBZHQJk9S6K+BYp1fZQoaAZoCWgPQwhgHccPVTZyQJSGlFKUaBVL32gWR0CZPUvLX+VDdX2UKGgGaAloD0MIGlJF8apBcUCUhpRSlGgVS89oFkdAmT3vAj6eoXV9lChoBmgJaA9DCMxCO6fZ+HJAlIaUUpRoFUu9aBZHQJk+G0ngHeJ1fZQoaAZoCWgPQwgHtd/ayQtyQJSGlFKUaBVL12gWR0CZRWUx20RfdX2UKGgGaAloD0MI7j7HR0upc0CUhpRSlGgVS+FoFkdAmUV4REnb7HV9lChoBmgJaA9DCG4yqgxjM29AlIaUUpRoFUvIaBZHQJlFimtQsPJ1fZQoaAZoCWgPQwi8XS9N0fFxQJSGlFKUaBVLuWgWR0CZReAYpDu0dX2UKGgGaAloD0MIjGmme50ETECUhpRSlGgVS5loFkdAmUX2PDHfdnV9lChoBmgJaA9DCI6SV+cYoXJAlIaUUpRoFU0LAWgWR0CZRfojv/ipdX2UKGgGaAloD0MIjgWFQdlIcUCUhpRSlGgVS+BoFkdAmUYBR2r4nHV9lChoBmgJaA9DCF0WE5vPinNAlIaUUpRoFUvBaBZHQJlGW5paibl1fZQoaAZoCWgPQwh4mzdOCvxxQJSGlFKUaBVL0mgWR0CZRrMt9QXRdX2UKGgGaAloD0MIT8sPXOVFckCUhpRSlGgVS+toFkdAmUa2+bmU4nV9lChoBmgJaA9DCDSeCOK8HnFAlIaUUpRoFUvHaBZHQJlGyHaews51ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 976,
+    "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 sha256:1dfa2c9d4891de8647c0ca973d31669771e676039c8a1b91a80c84df27394810
+size 84637

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:290b107d53aaa4bcc805f7dafe30c7d493a4554e94cf0927661b01d070f3f2bc
+size 43073

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-5.13.0-40-generic-x86_64-with-debian-bullseye-sid #45~20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022
+Python: 3.7.10
+Stable-Baselines3: 1.5.1a5
+PyTorch: 1.11.0
+GPU Enabled: False
+Numpy: 1.21.2
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7dba0ccacf20c48b1f80bec3bcc215fa698c08f279c4471ef1a31f098200b52a
+size 190214

results.json

@@ -0,0 +1 @@
+{"mean_reward": 283.4927351, "std_reward": 13.741124889719101, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T00:25:40.334631"}