Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 233.26 +/- 50.41
+      value: 277.76 +/- 15.03
       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 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 0x7fe684bec820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe684bec8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe684bec940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe684bec9d0>", "_build": "<function ActorCriticPolicy._build at 0x7fe684beca60>", "forward": "<function ActorCriticPolicy.forward at 0x7fe684becaf0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe684becb80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe684becc10>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe684becca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe684becd30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe684becdc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe684bece50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe684be5ab0>"}, "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": 1674039915755557673, "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:": "gAWVfxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIpoC0/4EXb0CUhpRSlIwBbJRNOQGMAXSUR0CRn9N5dGAkdX2UKGgGaAloD0MITx+BP3xicUCUhpRSlGgVTUsBaBZHQJGgMBo24ut1fZQoaAZoCWgPQwhXBP9bCaFxQJSGlFKUaBVNZwFoFkdAkaBxjjJdSnV9lChoBmgJaA9DCO2DLAumDm9AlIaUUpRoFU2bAWgWR0CRotYBeXzEdX2UKGgGaAloD0MI16IFaJtEcUCUhpRSlGgVTegBaBZHQJGja8RL9Mt1fZQoaAZoCWgPQwihvI+j+RdxQJSGlFKUaBVNSQFoFkdAkaO2SU1Q7HV9lChoBmgJaA9DCEuRfCUQA3BAlIaUUpRoFU1IAWgWR0CRpNn7HhjwdX2UKGgGaAloD0MIzCpsBjgMcUCUhpRSlGgVTVYBaBZHQJGk49cKPXF1fZQoaAZoCWgPQwiN0xBV+ARvQJSGlFKUaBVNWAFoFkdAkaZ4JZ4fOnV9lChoBmgJaA9DCKYJ209GyG9AlIaUUpRoFU0gAWgWR0CRptATZg5SdX2UKGgGaAloD0MIglMfSB4HcECUhpRSlGgVTWABaBZHQJGm9IPK+zt1fZQoaAZoCWgPQwgMIef9vzxxQJSGlFKUaBVNIgFoFkdAkadlfeDWb3V9lChoBmgJaA9DCCfBG9Kor25AlIaUUpRoFU03AWgWR0CRp/kKeCkHdX2UKGgGaAloD0MIIeo+ACm7b0CUhpRSlGgVTXUBaBZHQJGqGyVv/BF1fZQoaAZoCWgPQwgYlGk0eT5yQJSGlFKUaBVNVAFoFkdAkap4/FBIF3V9lChoBmgJaA9DCLZoAdrWlG9AlIaUUpRoFU1ZAWgWR0CRq+Xu3MINdX2UKGgGaAloD0MICTiEKjW9cUCUhpRSlGgVTXABaBZHQJGsAAyVObl1fZQoaAZoCWgPQwgOaVTgZEpwQJSGlFKUaBVNWwFoFkdAkaymmYSg5HV9lChoBmgJaA9DCCqPboRFwW1AlIaUUpRoFU09AWgWR0CRriCyQgcMdX2UKGgGaAloD0MIvayJBT43bkCUhpRSlGgVTTABaBZHQJGuQDs+mnB1fZQoaAZoCWgPQwgxQ+OJoCpxQJSGlFKUaBVNlgFoFkdAka6gwsXiznV9lChoBmgJaA9DCMoZijveZGxAlIaUUpRoFU04AWgWR0CRrtPSlWOqdX2UKGgGaAloD0MIBHRfzmyFbUCUhpRSlGgVTSsBaBZHQJGxJPP9kz51fZQoaAZoCWgPQwhv1ArT9wdsQJSGlFKUaBVNWwFoFkdAkbFBlUZNwnV9lChoBmgJaA9DCE3Z6Qf1l29AlIaUUpRoFU1iAWgWR0CRsXVrhzeXdX2UKGgGaAloD0MIwvwVMlcIUkCUhpRSlGgVS9FoFkdAkbH+LWI42nV9lChoBmgJaA9DCPhSeNDsB29AlIaUUpRoFU0vAWgWR0CRsjYNRWLhdX2UKGgGaAloD0MIM1LvqdxRcECUhpRSlGgVTSIBaBZHQJGyYdQwbl11fZQoaAZoCWgPQwh1PjxLkI9tQJSGlFKUaBVNUwFoFkdAkbKzN+so2HV9lChoBmgJaA9DCNMuppnu225AlIaUUpRoFU1ZAWgWR0CRsvXnhbW3dX2UKGgGaAloD0MIlufB3VlpcUCUhpRSlGgVTRgBaBZHQJG1xA8jiXJ1fZQoaAZoCWgPQwgJpppZS+U6QJSGlFKUaBVNMAFoFkdAkbX+WfK6nXV9lChoBmgJaA9DCAT/W8nOVHFAlIaUUpRoFU2FAWgWR0CRtxjeKsMidX2UKGgGaAloD0MIG53zU9yGcECUhpRSlGgVTSMBaBZHQJG3mkJrtVt1fZQoaAZoCWgPQwjAzHfwk0FvQJSGlFKUaBVNIQFoFkdAkbfkWZZ0S3V9lChoBmgJaA9DCGxAhLhyFnBAlIaUUpRoFU1sAWgWR0CRuAEBsANodX2UKGgGaAloD0MIkfP+P067bkCUhpRSlGgVTTEBaBZHQJG4okv9LpR1fZQoaAZoCWgPQwhnDkktlHJMQJSGlFKUaBVL72gWR0CRuOwDvE0jdX2UKGgGaAloD0MI6Po+HCQ0QkCUhpRSlGgVTQ4BaBZHQJG5luYQarF1fZQoaAZoCWgPQwhDOGbZU8RwQJSGlFKUaBVNEgFoFkdAkbnT4cm0FHV9lChoBmgJaA9DCP/nMF9eBm1AlIaUUpRoFU2WAWgWR0CRuz9oexOddX2UKGgGaAloD0MI88tgjMjxbkCUhpRSlGgVTSIBaBZHQJG7gsjFAFB1fZQoaAZoCWgPQwjHKqVnesRvQJSGlFKUaBVNGwFoFkdAkbwD8gpz93V9lChoBmgJaA9DCOfG9ITlcHBAlIaUUpRoFU1XAWgWR0CRvQppeu3ddX2UKGgGaAloD0MI1sVtNMBQcUCUhpRSlGgVTWMBaBZHQJG9MfwI+nt1fZQoaAZoCWgPQwi++Q0TzWVxQJSGlFKUaBVNZwFoFkdAkb4VU6xPf3V9lChoBmgJaA9DCEPGo1RCqW5AlIaUUpRoFU0oAWgWR0CRv6lnyup0dX2UKGgGaAloD0MIGmoUkoyQckCUhpRSlGgVTToBaBZHQJHADy9VWCF1fZQoaAZoCWgPQwgwgPChxCZxQJSGlFKUaBVNHAFoFkdAkcEbPD50sHV9lChoBmgJaA9DCPtbAvAPWHFAlIaUUpRoFU0/AWgWR0CR1JqoZQ54dX2UKGgGaAloD0MIWB6kpwgEcUCUhpRSlGgVTTwBaBZHQJHU9/Aj6ep1fZQoaAZoCWgPQwhAprVprPByQJSGlFKUaBVNAgFoFkdAkdUQB5ooNXV9lChoBmgJaA9DCDhMNEjBm3BAlIaUUpRoFU0dAWgWR0CR1TImPYFrdX2UKGgGaAloD0MIV3cstskQb0CUhpRSlGgVTVIBaBZHQJHWf+98JD51fZQoaAZoCWgPQwizBu+rsoZwQJSGlFKUaBVNcQFoFkdAkdbfKZDzAnV9lChoBmgJaA9DCGO3zyqz725AlIaUUpRoFU1VAWgWR0CR177hegL7dX2UKGgGaAloD0MIRGtFm2PTbUCUhpRSlGgVTR4BaBZHQJHYGYb83uN1fZQoaAZoCWgPQwiX5lYI6/pxQJSGlFKUaBVNQAFoFkdAkdir+PzWgHV9lChoBmgJaA9DCKXbErng7G9AlIaUUpRoFU1IAWgWR0CR2LZAIIGAdX2UKGgGaAloD0MIczCbAEPhb0CUhpRSlGgVTR8BaBZHQJHZOeumrKh1fZQoaAZoCWgPQwhtjnObcE9xQJSGlFKUaBVNIQFoFkdAkdoqoZQ53nV9lChoBmgJaA9DCLnCu1zEM3FAlIaUUpRoFU1FAWgWR0CR2j2Dxsl+dX2UKGgGaAloD0MIar3faMcacUCUhpRSlGgVTRYBaBZHQJHbTwlSjxl1fZQoaAZoCWgPQwhVpS2ucW9yQJSGlFKUaBVNMwFoFkdAkd3FdLQHA3V9lChoBmgJaA9DCHB5rBmZH3FAlIaUUpRoFU1hAWgWR0CR3nDGcWj5dX2UKGgGaAloD0MI8BezJWtXcUCUhpRSlGgVTTMBaBZHQJHe4IZ62OR1fZQoaAZoCWgPQwjmCBnIM1tuQJSGlFKUaBVNWAFoFkdAkd+63RXwLHV9lChoBmgJaA9DCJWe6SWGsHJAlIaUUpRoFU0ZAWgWR0CR37rl/6O6dX2UKGgGaAloD0MI5C1XP7bla0CUhpRSlGgVTV8BaBZHQJHgrVVghKV1fZQoaAZoCWgPQwjX+bfLfihvQJSGlFKUaBVNggFoFkdAkeGvVurIYHV9lChoBmgJaA9DCGrZWl8kyW1AlIaUUpRoFU1LAWgWR0CR4gOXmeUZdX2UKGgGaAloD0MIZD+Lpchzb0CUhpRSlGgVTUUBaBZHQJHi0XpGFzx1fZQoaAZoCWgPQwgoDTUKSc5tQJSGlFKUaBVNDgFoFkdAkeO3GKhtcnV9lChoBmgJaA9DCPs+HCTEtG9AlIaUUpRoFU03AWgWR0CR4/uW8h9tdX2UKGgGaAloD0MIWKoLeBnOcUCUhpRSlGgVTWEBaBZHQJHkHYpUgjh1fZQoaAZoCWgPQwiUE+0qJC5xQJSGlFKUaBVNWwFoFkdAkeSIlpoK2XV9lChoBmgJaA9DCMWqQZjbFm1AlIaUUpRoFU1sAWgWR0CR5PqRlpXZdX2UKGgGaAloD0MIILdfPlm8cECUhpRSlGgVTUkBaBZHQJHmhLytmth1fZQoaAZoCWgPQwiCyvj3GdFxQJSGlFKUaBVNjQFoFkdAkedzuBtk4HV9lChoBmgJaA9DCL7dkhww52xAlIaUUpRoFU0rAWgWR0CR6FhsqJ/HdX2UKGgGaAloD0MIsWzmkBTQcECUhpRSlGgVTU8BaBZHQJHpBYQrc0t1fZQoaAZoCWgPQwgPml33VhduQJSGlFKUaBVNKAFoFkdAkelffj0cwXV9lChoBmgJaA9DCLXeb7RjW3BAlIaUUpRoFU1WAWgWR0CR6hcv/R3NdX2UKGgGaAloD0MI2CrB4nDuRUCUhpRSlGgVTScBaBZHQJHrX4gzP8h1fZQoaAZoCWgPQwhF8L+VLExyQJSGlFKUaBVNgQFoFkdAkexa77Kq43V9lChoBmgJaA9DCAUXK2qwAW9AlIaUUpRoFU18AWgWR0CR7SHbh3qzdX2UKGgGaAloD0MI5jv4icNscECUhpRSlGgVTWUBaBZHQJHtSBtk4FR1fZQoaAZoCWgPQwhQOSaL+xtwQJSGlFKUaBVNRgFoFkdAke1PS+g133V9lChoBmgJaA9DCKeufJZnenFAlIaUUpRoFU0NAWgWR0CR7a51Ng0CdX2UKGgGaAloD0MI8RDGT+NxcECUhpRSlGgVTTgBaBZHQJHtuEUTL4h1fZQoaAZoCWgPQwjnNAu0ux9uQJSGlFKUaBVNOAFoFkdAke4P8AJb+3V9lChoBmgJaA9DCBRcrKjBeW9AlIaUUpRoFU1mAWgWR0CR7+EVnEl3dX2UKGgGaAloD0MIWfj6WhfcbUCUhpRSlGgVTYYBaBZHQJHwU4n4O+Z1fZQoaAZoCWgPQwgH0sWmVc5wQJSGlFKUaBVNVAFoFkdAkfF8/hVENXV9lChoBmgJaA9DCKd38X5c1HFAlIaUUpRoFU1DAWgWR0CR8dw1zhgmdX2UKGgGaAloD0MIzA2GOiyUbkCUhpRSlGgVTT0BaBZHQJHz6FmFrVR1fZQoaAZoCWgPQwi45SMpaSNtQJSGlFKUaBVNYAFoFkdAkfQUOmR/3HV9lChoBmgJaA9DCD+rzJRWGm1AlIaUUpRoFU0MAWgWR0CR9GO8CgbqdX2UKGgGaAloD0MIGtzWFh6EbECUhpRSlGgVTUsBaBZHQJH1NV3ljmV1fZQoaAZoCWgPQwjzqs5qAUpyQJSGlFKUaBVNcQFoFkdAkfVWt+1Bt3VlLg=="}, "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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "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 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 0x7f5c319b6820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5c319b68b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5c319b6940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5c319b69d0>", "_build": "<function ActorCriticPolicy._build at 0x7f5c319b6a60>", "forward": "<function ActorCriticPolicy.forward at 0x7f5c319b6af0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5c319b6b80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5c319b6c10>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5c319b6ca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5c319b6d30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5c319b6dc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5c319b6e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5c319b29c0>"}, "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": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674057907551847893, "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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 736, "n_steps": 512, "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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4daa6dfeb1b526fc03dd6ef028c5b121a33c5b2469e1afdee89e818eeba9dac5
-size 147424
+oid sha256:61c886940bfb464a536a015928bd362f5c120ea333e5e4376f8e9bfb88247423
+size 147499

ppo-LunarLander-v2/data

@@ -4,20 +4,20 @@
         ":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 0x7fe684bec820>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe684bec8b0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe684bec940>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe684bec9d0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fe684beca60>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fe684becaf0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe684becb80>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe684becc10>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fe684becca0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe684becd30>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe684becdc0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe684bece50>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f5c319b6820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5c319b68b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5c319b6940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5c319b69d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5c319b6a60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5c319b6af0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5c319b6b80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5c319b6c10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5c319b6ca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5c319b6d30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5c319b6dc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5c319b6e50>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fe684be5ab0>"
+        "_abc_impl": "<_abc_data object at 0x7f5c319b29c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -43,12 +43,12 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 1015808,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 1507328,
+    "_total_timesteps": 1500000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1674039915755557673,
+    "start_time": 1674057907551847893,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -67,17 +67,17 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": -0.004885333333333408,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 248,
-    "n_steps": 1024,
+    "_n_updates": 736,
+    "n_steps": 512,
     "gamma": 0.999,
     "gae_lambda": 0.98,
     "ent_coef": 0.01,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:68cff6b05ad804c9159d5728b5b6fd94350f60c97969fc0299ded9bd68b7525d
-size 87929
+oid sha256:47f217e9d4686b3123a140ad09aa0e27c1d25afacb62988f65dd9a1195aea8d2
+size 88057

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a3501ef19a8ebbd2a2747229c1bd3c3b5f171bb2baad8e6865bf55b04c10c30d
+oid sha256:0230669bc3e9c0d763105985868216d26e764f79f9cd82eec5a57d1daa28940e
 size 43393

ppo-LunarLander-v2/system_info.txt

@@ -1,7 +1,7 @@
-- OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
-- Python: 3.8.16
+- 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: 1.13.0+cu116
+- PyTorch: 1.13.1+cu116
 - GPU Enabled: True
 - Numpy: 1.21.6
 - Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 233.26234635329502, "std_reward": 50.40604433407172, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T11:31:50.290298"}
+{"mean_reward": 277.757103745115, "std_reward": 15.034564013712588, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T16:52:27.022183"}