creating my first RL model

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 245.85 +/- 32.38
+      value: 292.67 +/- 19.92
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +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 0x7fdac1fbef70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdac1fc3040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdac1fc30d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdac1fc3160>", "_build": "<function ActorCriticPolicy._build at 0x7fdac1fc31f0>", "forward": "<function ActorCriticPolicy.forward at 0x7fdac1fc3280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdac1fc3310>", "_predict": "<function ActorCriticPolicy._predict at 0x7fdac1fc33a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdac1fc3430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdac1fc34c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdac1fc3550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fdac1fc0240>"}, "verbose": 0, "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": 100007936, "_total_timesteps": 100000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671987826711126267, "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": -7.935999999997279e-05, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVcBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIVYmytxRqYUCUhpRSlIwBbJRN6AOMAXSUR0DrozbItDlYdX2UKGgGaAloD0MIca5hhoaGcUCUhpRSlGgVTf8BaBZHQOujPYJAt4B1fZQoaAZoCWgPQwhssHCS5rlvQJSGlFKUaBVNcgFoFkdA66M+H/1g6XV9lChoBmgJaA9DCCtpxTeUN29AlIaUUpRoFU1vAmgWR0Dro1qHJLdvdX2UKGgGaAloD0MIibZj6q6NbUCUhpRSlGgVS9ZoFkdA66Nz9NWU8nV9lChoBmgJaA9DCAnh0caR6G9AlIaUUpRoFU2DAWgWR0Dro5MBun/DdX2UKGgGaAloD0MInwJgPMPRckCUhpRSlGgVTRUCaBZHQOujlbD2rXF1fZQoaAZoCWgPQwgsgZTYtb5sQJSGlFKUaBVNCAFoFkdA66Os34Kx93V9lChoBmgJaA9DCP2H9NvXanFAlIaUUpRoFU3GAWgWR0Dro7BLpRoAdX2UKGgGaAloD0MIfotOllqkYUCUhpRSlGgVTegDaBZHQOujsjJp35h1fZQoaAZoCWgPQwi0zCIUW2puQJSGlFKUaBVNqgFoFkdA66O3zND+i3V9lChoBmgJaA9DCCarItxkGXBAlIaUUpRoFU0/AWgWR0Dro9iBOHnEdX2UKGgGaAloD0MIDkqYafunYECUhpRSlGgVTegDaBZHQOuj38SK3ux1fZQoaAZoCWgPQwg/4les4URpQJSGlFKUaBVNRQNoFkdA66PtfXoTwnV9lChoBmgJaA9DCGMmUS949G5AlIaUUpRoFU0cAWgWR0DrpA8taIN3dX2UKGgGaAloD0MIzxH5LqVaXUCUhpRSlGgVTegDaBZHQOukGTiADq51fZQoaAZoCWgPQwggnE8dK8hsQJSGlFKUaBVN2gFoFkdA66Qrje0ojXV9lChoBmgJaA9DCG7eOCnM+2ZAlIaUUpRoFU3oA2gWR0DrpC+ESM99dX2UKGgGaAloD0MIHHv2XKbgbUCUhpRSlGgVS+BoFkdA66Q2UW/JvHV9lChoBmgJaA9DCDbmdcShT2BAlIaUUpRoFU3oA2gWR0DrpD93/xUedX2UKGgGaAloD0MIJZLoZVSOckCUhpRSlGgVTcgBaBZHQOukQcEHMU11fZQoaAZoCWgPQwj1gk9z8sFiQJSGlFKUaBVN6ANoFkdA66REerMkhXV9lChoBmgJaA9DCFa3ek56TGFAlIaUUpRoFU3oA2gWR0DrpEuEU0vXdX2UKGgGaAloD0MI02cHXFc1bkCUhpRSlGgVTZ4DaBZHQOukS5ybQTp1fZQoaAZoCWgPQwgcDHVYIWlxQJSGlFKUaBVN/AFoFkdA66RMkw35vnV9lChoBmgJaA9DCPa1LjXCj2BAlIaUUpRoFU3oA2gWR0DrpFIDcM3IdX2UKGgGaAloD0MIEoPAyqETTUCUhpRSlGgVS4loFkdA66Rgfa6BiHV9lChoBmgJaA9DCHCyDdzBuXBAlIaUUpRoFU0aAWgWR0DrpHuPDHfedX2UKGgGaAloD0MIhBJm2n5tcECUhpRSlGgVS7toFkdA66R89yDIzXV9lChoBmgJaA9DCIvfFFaqgG9AlIaUUpRoFU17AWgWR0DrpKE6UaAGdX2UKGgGaAloD0MIk1LQ7aW9cUCUhpRSlGgVTf8BaBZHQOukvG40/GF1fZQoaAZoCWgPQwiFQC5x5PdfQJSGlFKUaBVN6ANoFkdA66S/EKE39HV9lChoBmgJaA9DCJRNucK722xAlIaUUpRoFU3dAWgWR0DrpMaJjUd8dX2UKGgGaAloD0MIqkavBmhbckCUhpRSlGgVTR4CaBZHQOukyv1DjR51fZQoaAZoCWgPQwjc8pGU9G5YQJSGlFKUaBVN6ANoFkdA66TeI8yN43V9lChoBmgJaA9DCCMShZb1/G9AlIaUUpRoFU0KA2gWR0DrpQKL+glGdX2UKGgGaAloD0MIQGt+/OUFcECUhpRSlGgVS99oFkdA66UGRA0KqnV9lChoBmgJaA9DCAaBlUOLGWFAlIaUUpRoFU3oA2gWR0DrpQj5Ec81dX2UKGgGaAloD0MIak/JOTEEZ0CUhpRSlGgVTegDaBZHQOulEEVUMod1fZQoaAZoCWgPQwjDt7Bu/IFwQJSGlFKUaBVNnQNoFkdA66UoBgE2YXV9lChoBmgJaA9DCEBOmDDakXBAlIaUUpRoFU1CAmgWR0DrpWIK77KrdX2UKGgGaAloD0MIV0EMdO3acUCUhpRSlGgVTdoBaBZHQOulZ8tbs4V1fZQoaAZoCWgPQwhgd7rzxNpwQJSGlFKUaBVNogFoFkdA66Vo4ku6E3V9lChoBmgJaA9DCBrEB3Z84HBAlIaUUpRoFUv0aBZHQOulfhAfMfR1fZQoaAZoCWgPQwgZ4lgXN1djQJSGlFKUaBVN6ANoFkdA66WDXE61cHV9lChoBmgJaA9DCLZlwFlKbWFAlIaUUpRoFU3oA2gWR0DrpYN3fyf+dX2UKGgGaAloD0MIxsTm49pjZUCUhpRSlGgVTegDaBZHQOulhMaIeo11fZQoaAZoCWgPQwgMOiF00GFQQJSGlFKUaBVLsWgWR0DrpaGyfthNdX2UKGgGaAloD0MI4pS5+UbnXECUhpRSlGgVTegDaBZHQOulolqk/KR1fZQoaAZoCWgPQwiPHOkMzNhxQJSGlFKUaBVN7gFoFkdA66WkbQb++HV9lChoBmgJaA9DCIUHza77i3BAlIaUUpRoFUvcaBZHQOulqDcj7hx1fZQoaAZoCWgPQwguH0lJj3NvQJSGlFKUaBVL0mgWR0DrpbjiOvMbdX2UKGgGaAloD0MIayxhbYyMW0CUhpRSlGgVTegDaBZHQOulvtWjoIR1fZQoaAZoCWgPQwikGvZ7Yi1gQJSGlFKUaBVN6ANoFkdA66XAVTR6W3V9lChoBmgJaA9DCNxoAG+BzG1AlIaUUpRoFUveaBZHQOulwaXMQmN1fZQoaAZoCWgPQwhWgsXhjDhyQJSGlFKUaBVNCgFoFkdA66XKiE6DG3V9lChoBmgJaA9DCLtemiIA2XBAlIaUUpRoFU1pAWgWR0Drpc/DPWxydX2UKGgGaAloD0MIyT1d3fHfcECUhpRSlGgVTSwBaBZHQOul7v0AcT91fZQoaAZoCWgPQwjwplt2CLFiQJSGlFKUaBVN6ANoFkdA66XxZfMOgHV9lChoBmgJaA9DCCfYf50b+WNAlIaUUpRoFU3oA2gWR0DrpfeQU5+6dX2UKGgGaAloD0MI8guvJHn+bkCUhpRSlGgVTU0BaBZHQOul/lnyup11fZQoaAZoCWgPQwjAWyBBcQNyQJSGlFKUaBVN4AFoFkdA66YCMtCiRHV9lChoBmgJaA9DCKgck8W9EXBAlIaUUpRoFU1gAWgWR0Drph1C2tuDdX2UKGgGaAloD0MICmgibHhqcECUhpRSlGgVS8hoFkdA66YmkLH+63V9lChoBmgJaA9DCESHwJFA6GBAlIaUUpRoFU3oA2gWR0Drpi3mmtQsdX2UKGgGaAloD0MIyatzDMgaY0CUhpRSlGgVTegDaBZHQOumL/5N47l1fZQoaAZoCWgPQwhpNo/D4P5gQJSGlFKUaBVN6ANoFkdA66Y2CyyD7XV9lChoBmgJaA9DCH6nyYy3V3FAlIaUUpRoFU0kAmgWR0DrpjYYGdI5dX2UKGgGaAloD0MI6dZrelCackCUhpRSlGgVTVMCaBZHQOumPpEF4cF1fZQoaAZoCWgPQwjPFDqvsZxxQJSGlFKUaBVLyGgWR0DrplB66asqdX2UKGgGaAloD0MIAALWqt3JbECUhpRSlGgVS/toFkdA66Zm91dPcnV9lChoBmgJaA9DCNbm/1VHr1FAlIaUUpRoFUuraBZHQOumgxPbfxd1fZQoaAZoCWgPQwgaprbUwcBtQJSGlFKUaBVNXAFoFkdA66ao0J4SpXV9lChoBmgJaA9DCHmu78NBTXBAlIaUUpRoFU3DAWgWR0DrpsIpjMFEdX2UKGgGaAloD0MIjV2iemtHbkCUhpRSlGgVTdgCaBZHQOum3v5FgD11fZQoaAZoCWgPQwh0CvKzEQxnQJSGlFKUaBVN6ANoFkdA66bfldTo+3V9lChoBmgJaA9DCDI4Sl4dl2JAlIaUUpRoFU3oA2gWR0DrpubjKgZkdX2UKGgGaAloD0MIRfC/lezxYECUhpRSlGgVTegDaBZHQOum6Jsyi251fZQoaAZoCWgPQwiBI4EGG8dgQJSGlFKUaBVN6ANoFkdA66b2u/tY0XV9lChoBmgJaA9DCDljmBM0N2JAlIaUUpRoFU3oA2gWR0Drpv1L1VYIdX2UKGgGaAloD0MIEoWWdf+RW0CUhpRSlGgVTegDaBZHQOunJFhJAdJ1fZQoaAZoCWgPQwimJsEbUu5gQJSGlFKUaBVN6ANoFkdA66crYyGi6HV9lChoBmgJaA9DCBy1wvQ963BAlIaUUpRoFU2hAWgWR0DrpzPEqlP8dX2UKGgGaAloD0MI26M33EfVXECUhpRSlGgVTegDaBZHQOunN19Wp611fZQoaAZoCWgPQwh40Oy693FxQJSGlFKUaBVL52gWR0DrpzvdIGyHdX2UKGgGaAloD0MIqaPjamTmb0CUhpRSlGgVTTgBaBZHQOunRfwRXfZ1fZQoaAZoCWgPQwi1boPaLztwQJSGlFKUaBVNUAFoFkdA66dLRfOUuHV9lChoBmgJaA9DCMR4zau6BG1AlIaUUpRoFU1nAWgWR0Drp0vpnpSrdX2UKGgGaAloD0MIn+dPG9UPXUCUhpRSlGgVTegDaBZHQOunYVNQCS11fZQoaAZoCWgPQwjThVj9ERBvQJSGlFKUaBVNCgNoFkdA66diqBmPHXV9lChoBmgJaA9DCOpdvB+3IltAlIaUUpRoFU3oA2gWR0Drp2OydnTRdX2UKGgGaAloD0MIsKvJUxavcUCUhpRSlGgVS99oFkdA66doS1eBx3V9lChoBmgJaA9DCNL8Ma1NsWFAlIaUUpRoFU3oA2gWR0Drp2jALRa5dX2UKGgGaAloD0MIbeUl/xMxbkCUhpRSlGgVTQwBaBZHQOuneyWszVN1fZQoaAZoCWgPQwhdGr/wyuFvQJSGlFKUaBVLxmgWR0Drp3wLux8ldX2UKGgGaAloD0MIER0CR4KqakCUhpRSlGgVTUQDaBZHQOung/40uUV1fZQoaAZoCWgPQwg0D2CRX1xsQJSGlFKUaBVNxwFoFkdA66eZL4nF53V9lChoBmgJaA9DCKJinL+JSG9AlIaUUpRoFU0TAWgWR0Drp6L00WM1dX2UKGgGaAloD0MIRl7WxIIGb0CUhpRSlGgVTQUBaBZHQOunpz2tdRl1fZQoaAZoCWgPQwgpWrkXGIhsQJSGlFKUaBVNJwFoFkdA66eqqGUOeHVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 24416, "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": 4096, "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-5.4.0-135-generic-x86_64-with-glibc2.17 #152~18.04.2-Ubuntu SMP Tue Nov 29 08:23:49 UTC 2022", "Python": "3.8.13", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1", "GPU Enabled": "True", "Numpy": "1.23.1", "Gym": "0.21.0"}}
+{"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 0x7fc64e352ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc64e352d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc64e352dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc64e352e50>", "_build": "<function ActorCriticPolicy._build at 0x7fc64e352ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc64e352f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc64e357040>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc64e3570d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc64e357160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc64e3571f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc64e357280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc64e34d840>"}, "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": 100007936, "_total_timesteps": 100000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672213226022385047, "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": -7.935999999997279e-05, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 24416, "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-5.4.0-135-generic-x86_64-with-glibc2.17 #152~18.04.2-Ubuntu SMP Tue Nov 29 08:23:49 UTC 2022", "Python": "3.8.13", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1", "GPU Enabled": "True", "Numpy": "1.23.1", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cc5947ea0c8582b6b643ffebdd95d16a043b4f93b5b7ea93549495e975fcdd0d
-size 147274
+oid sha256:27c93bf8fcc298000c1eba8ba66582add341c8b8c0da93fa286660fcacb4c120
+size 147160

ppo-LunarLander-v2/data

@@ -4,21 +4,21 @@
         ":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 0x7fdac1fbef70>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdac1fc3040>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdac1fc30d0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdac1fc3160>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fdac1fc31f0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fdac1fc3280>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdac1fc3310>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fdac1fc33a0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdac1fc3430>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdac1fc34c0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdac1fc3550>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fc64e352ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc64e352d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc64e352dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc64e352e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc64e352ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc64e352f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc64e357040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc64e3570d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc64e357160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc64e3571f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc64e357280>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fdac1fc0240>"
+        "_abc_impl": "<_abc_data object at 0x7fc64e34d840>"
     },
-    "verbose": 0,
+    "verbose": 1,
     "policy_kwargs": {},
     "observation_space": {
         ":type:": "<class 'gym.spaces.box.Box'>",
@@ -47,7 +47,7 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1671987826711126267,
+    "start_time": 1672213226022385047,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -69,7 +69,7 @@
     "_current_progress_remaining": -7.935999999997279e-05,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -82,7 +82,7 @@
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 4096,
+    "batch_size": 64,
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d4d6d0b9353309427d9b20c0b2237c64bc111053c8f63ddc0344a85c8defe557
+oid sha256:455e8b06f1abad09b947328c562d87891037c504ffd36de9f20516e2559624d6
 size 87865

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:29aa854f36fcbb502a5bd296c452549bc9233f89c70f2bdb8cc7a67d37030693
+oid sha256:45cb2a5a8a209cb31ecf21dcb7eccbc7fa65ef84ced2396317cc266a4e390e7c
 size 43201

results.json

@@ -1 +1 @@
-{"mean_reward": 245.84613729976883, "std_reward": 32.37961112228234, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-26T17:47:53.929588"}
+{"mean_reward": 292.6699418393399, "std_reward": 19.915744954811448, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-29T04:27:53.098341"}