Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 248.49 +/- 23.28
+      value: -196.74 +/- 197.52
       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 0x7ff8747af670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff8747af700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff8747af790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff8747af820>", "_build": "<function ActorCriticPolicy._build at 0x7ff8747af8b0>", "forward": "<function ActorCriticPolicy.forward at 0x7ff8747af940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff8747af9d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff8747afa60>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff8747afaf0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff8747afb80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff8747afc10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff8747afca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff87479a8d0>"}, "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": 1673908799896429079, "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:": "gAWVfhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIbOhmf2BOckCUhpRSlIwBbJRN3wGMAXSUR0CSLXs2vStvdX2UKGgGaAloD0MIf2jmybVubkCUhpRSlGgVTWsBaBZHQJIttAnlXBB1fZQoaAZoCWgPQwgRGyycpJNyQJSGlFKUaBVNQwFoFkdAki21Y+0PYnV9lChoBmgJaA9DCMqmXOGddnBAlIaUUpRoFU0XAWgWR0CSLgcer+5wdX2UKGgGaAloD0MI1qvI6ADJcECUhpRSlGgVTRwBaBZHQJIuuksSTQp1fZQoaAZoCWgPQwjFqkGY20RtQJSGlFKUaBVNsQFoFkdAki++Lm6oVHV9lChoBmgJaA9DCI9U3/lF3GtAlIaUUpRoFU1EAWgWR0CSMPaY/mkndX2UKGgGaAloD0MIvJaQD3p/bkCUhpRSlGgVTToBaBZHQJIxGTwDvE11fZQoaAZoCWgPQwgXnpeKDXxxQJSGlFKUaBVNsgFoFkdAkjFEgSvkinV9lChoBmgJaA9DCF6hD5axeXFAlIaUUpRoFU1IAWgWR0CSMi/PgNwzdX2UKGgGaAloD0MIWixF8hUwbUCUhpRSlGgVTXEBaBZHQJIyvqmj0th1fZQoaAZoCWgPQwjJyFnYU1JuQJSGlFKUaBVNPAFoFkdAkjRTpxFRYXV9lChoBmgJaA9DCJnWprF9L3BAlIaUUpRoFU0qAWgWR0CSSW5le4TcdX2UKGgGaAloD0MIGeQuwhQ+bkCUhpRSlGgVTSgBaBZHQJJK77DVH4J1fZQoaAZoCWgPQwjz5JoCmbttQJSGlFKUaBVNfQFoFkdAkku4+fRNRHV9lChoBmgJaA9DCIXtJ2M8BnFAlIaUUpRoFU3DAWgWR0CSTGCw8nuzdX2UKGgGaAloD0MIdvwXCIIUbkCUhpRSlGgVTVUBaBZHQJJNDmA9V3l1fZQoaAZoCWgPQwgGSZ9WEbBwQJSGlFKUaBVNeQFoFkdAkk2K6FuejHV9lChoBmgJaA9DCDEjvD0IJm1AlIaUUpRoFU1vAWgWR0CSThYEW69TdX2UKGgGaAloD0MIyoegavRackCUhpRSlGgVTXYBaBZHQJJOnjvNNah1fZQoaAZoCWgPQwiWICOgAklxQJSGlFKUaBVNKgFoFkdAkk7T850bLnV9lChoBmgJaA9DCADl796Ro3JAlIaUUpRoFU2JAWgWR0CST/EsJ6Y3dX2UKGgGaAloD0MIGf7TDZSSbECUhpRSlGgVTVEBaBZHQJJQhurIYFd1fZQoaAZoCWgPQwgEVaNXgydwQJSGlFKUaBVNLQFoFkdAklD++M6zV3V9lChoBmgJaA9DCKD+s+bH53FAlIaUUpRoFU1QAWgWR0CSUZbGFSKndX2UKGgGaAloD0MIC+4HPPCucUCUhpRSlGgVTRQBaBZHQJJR1A9mpVF1fZQoaAZoCWgPQwj356Ih40JvQJSGlFKUaBVNfQFoFkdAklHT5CWu5nV9lChoBmgJaA9DCBegbTVrGXBAlIaUUpRoFU2qAWgWR0CSUgQbMotudX2UKGgGaAloD0MIVkeOdIaTbkCUhpRSlGgVTUgBaBZHQJJVAlu3trt1fZQoaAZoCWgPQwgYBcHj2/1wQJSGlFKUaBVNIwFoFkdAklYslC1JDnV9lChoBmgJaA9DCBwMdVghgHFAlIaUUpRoFU0LAWgWR0CSVs+ajN6gdX2UKGgGaAloD0MIU0Kwql6GcECUhpRSlGgVTUYBaBZHQJJW2Km8/Ux1fZQoaAZoCWgPQwjkZU0s8EpxQJSGlFKUaBVNXgFoFkdAklcKkZaV2XV9lChoBmgJaA9DCGpMiLmkynJAlIaUUpRoFU03AWgWR0CSV2SL61stdX2UKGgGaAloD0MINKFJYsnCbkCUhpRSlGgVTToBaBZHQJJYyAuqWC51fZQoaAZoCWgPQwip+pXOB/ZwQJSGlFKUaBVNXwFoFkdAklkNlmOENHV9lChoBmgJaA9DCCIAOPastHBAlIaUUpRoFU0gAWgWR0CSWbDx9XtCdX2UKGgGaAloD0MIwXPv4VLEcUCUhpRSlGgVTUQBaBZHQJJbcNRWLgp1fZQoaAZoCWgPQwhLPnYX6BdwQJSGlFKUaBVNRgFoFkdAklwx6jWTYHV9lChoBmgJaA9DCB1WuOXjgXBAlIaUUpRoFU05AWgWR0CSXEh/iHZcdX2UKGgGaAloD0MIJsYy/RLVMkCUhpRSlGgVS/BoFkdAkl1EQ9RrJ3V9lChoBmgJaA9DCEcf8wGBEnFAlIaUUpRoFU2XAWgWR0CSXW6VMVUNdX2UKGgGaAloD0MIEk2giEXOcECUhpRSlGgVTZIBaBZHQJJfKFGoaUB1fZQoaAZoCWgPQwhf8GlOXtJvQJSGlFKUaBVNygFoFkdAkmFNvjwQUnV9lChoBmgJaA9DCFG7XwX4YnFAlIaUUpRoFU06AWgWR0CSYeayKNyYdX2UKGgGaAloD0MI3GW/7vRNa0CUhpRSlGgVTToBaBZHQJJikelsP8R1fZQoaAZoCWgPQwjhlo+kpNRvQJSGlFKUaBVNSgFoFkdAkmLAK8cuJ3V9lChoBmgJaA9DCHAIVWp2MXFAlIaUUpRoFU0uAWgWR0CSY7YkmhM8dX2UKGgGaAloD0MIdTv7ygN2cECUhpRSlGgVTUoBaBZHQJJlCwUxmCl1fZQoaAZoCWgPQwjfGAKAY88LwJSGlFKUaBVNBwFoFkdAkmUjHS4OMHV9lChoBmgJaA9DCF/Tg4ISKXBAlIaUUpRoFU1mAWgWR0CSZscbzbvgdX2UKGgGaAloD0MIJoqQul3WcECUhpRSlGgVTSkBaBZHQJJnDM/yGzt1fZQoaAZoCWgPQwimRuhnqoNyQJSGlFKUaBVN0QFoFkdAkmdNuxbB43V9lChoBmgJaA9DCEPGo1TCSnJAlIaUUpRoFU0jAWgWR0CSZ/hPj4pMdX2UKGgGaAloD0MIGavN/6vRbkCUhpRSlGgVTUoBaBZHQJJpGK4x1xN1fZQoaAZoCWgPQwik/nqFBdNqQJSGlFKUaBVNSQFoFkdAkmrwTIvJzXV9lChoBmgJaA9DCJBN8iO+YnJAlIaUUpRoFU0bAWgWR0CSbI4x1xKhdX2UKGgGaAloD0MIMsueBLYCcECUhpRSlGgVTT0BaBZHQJJsnwVj7Q91fZQoaAZoCWgPQwjqBgq8U4ZyQJSGlFKUaBVNKAFoFkdAkm01s54nnnV9lChoBmgJaA9DCO/Jw0ItpnJAlIaUUpRoFU05AWgWR0CSgYK7I1cddX2UKGgGaAloD0MIkL3e/bEPckCUhpRSlGgVTRoBaBZHQJKBr7cfvF51fZQoaAZoCWgPQwgKur2k8VpxQJSGlFKUaBVNlwFoFkdAkoMQ3T/hl3V9lChoBmgJaA9DCI7lXfWAXGNAlIaUUpRoFU3oA2gWR0CShEP/7zkIdX2UKGgGaAloD0MI2jo42FuscUCUhpRSlGgVTUgBaBZHQJKFEaGYa5x1fZQoaAZoCWgPQwjakH9mUPJwQJSGlFKUaBVNTwFoFkdAkoXohQm/nHV9lChoBmgJaA9DCMjvbfozRWxAlIaUUpRoFU1IAWgWR0CShmMR6F/QdX2UKGgGaAloD0MIH73hPnKScECUhpRSlGgVTQwBaBZHQJKJMiQkond1fZQoaAZoCWgPQwjiy0QRkspwQJSGlFKUaBVNEQFoFkdAkooYBaLXMHV9lChoBmgJaA9DCBbCaiwh0XBAlIaUUpRoFU0ZAWgWR0CSjGEQXhwVdX2UKGgGaAloD0MIv+5050kDcECUhpRSlGgVTYIBaBZHQJKMYlme18d1fZQoaAZoCWgPQwjusInMXGliQJSGlFKUaBVN6ANoFkdAko1PJzT4L3V9lChoBmgJaA9DCI4FhUEZjnBAlIaUUpRoFU1PAWgWR0CSkHwrDqGDdX2UKGgGaAloD0MIYkm5+5zhb0CUhpRSlGgVTU0BaBZHQJKR0C+10DF1fZQoaAZoCWgPQwiZ1NAG4CpvQJSGlFKUaBVNLgFoFkdAkpMCZ4Oc2HV9lChoBmgJaA9DCPVKWYY4s29AlIaUUpRoFU2yAWgWR0CSkxJDVpbmdX2UKGgGaAloD0MI0H6kiAzhYECUhpRSlGgVTegDaBZHQJKU4EwFkhB1fZQoaAZoCWgPQwifk943vgxwQJSGlFKUaBVNgQFoFkdAkpXK6z3RHHV9lChoBmgJaA9DCG7fo/76wW5AlIaUUpRoFU0zAWgWR0CSlgpAUtZndX2UKGgGaAloD0MIBWoxeJhBb0CUhpRSlGgVTSEBaBZHQJKWJreqJdl1fZQoaAZoCWgPQwjDLR9JSSFEQJSGlFKUaBVL/mgWR0CSl4FbFCLNdX2UKGgGaAloD0MIlIRE2gYLcUCUhpRSlGgVTXICaBZHQJKXqW+oLoh1fZQoaAZoCWgPQwgrFr8p7J1yQJSGlFKUaBVNawFoFkdAkpp4KMNtqHV9lChoBmgJaA9DCAZoW826IG9AlIaUUpRoFU0SAWgWR0CSmtfbblBAdX2UKGgGaAloD0MIhsd+FstMcECUhpRSlGgVTTcCaBZHQJKbT7MxGlR1fZQoaAZoCWgPQwj83xEVqptHQJSGlFKUaBVL/GgWR0CSnCvkRzzVdX2UKGgGaAloD0MICmXh62uwbkCUhpRSlGgVTZoBaBZHQJKcOhM8HOd1fZQoaAZoCWgPQwgeMuVDUOViQJSGlFKUaBVN6ANoFkdAkp15LIxQBXV9lChoBmgJaA9DCDYFMjsLTG1AlIaUUpRoFU0qAWgWR0CSnefq5byIdX2UKGgGaAloD0MIvJS6ZBwoZECUhpRSlGgVTegDaBZHQJKfbTnaFmF1fZQoaAZoCWgPQwhkz57LFMJxQJSGlFKUaBVNIQFoFkdAkqAOjqOcUnV9lChoBmgJaA9DCGL1RxjGAHBAlIaUUpRoFU2FAWgWR0CSoCgQ6IWQdX2UKGgGaAloD0MIjjo6rkbocUCUhpRSlGgVTUMBaBZHQJKgcPpY9xJ1fZQoaAZoCWgPQwisOUAwh4xwQJSGlFKUaBVNLgFoFkdAkqC8X3xnWnV9lChoBmgJaA9DCBR6/Un83WRAlIaUUpRoFU3oA2gWR0CSoXoUi6g/dX2UKGgGaAloD0MIVFT9SqcecUCUhpRSlGgVTVYBaBZHQJKiCxLTQVt1fZQoaAZoCWgPQwjIfECgMyZxQJSGlFKUaBVNSQFoFkdAkqMDMV1wHnV9lChoBmgJaA9DCMGRQIMNAXNAlIaUUpRoFU1KAWgWR0CSozPsRg7YdX2UKGgGaAloD0MI6SYxCKxdbECUhpRSlGgVTTwBaBZHQJKlzGNrCWN1fZQoaAZoCWgPQwhwsaIGE2VwQJSGlFKUaBVNOAFoFkdAkqYhnrY5DXV9lChoBmgJaA9DCKqdYWpLhG1AlIaUUpRoFU1RAWgWR0CSpjjRlYlqdWUu"}, "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 0x7d02b6a9dc60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d02b6a9dcf0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d02b6a9dd80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d02b6a9de10>", "_build": "<function ActorCriticPolicy._build at 0x7d02b6a9dea0>", "forward": "<function ActorCriticPolicy.forward at 0x7d02b6a9df30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d02b6a9dfc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d02b6a9e050>", "_predict": "<function ActorCriticPolicy._predict at 0x7d02b6a9e0e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d02b6a9e170>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d02b6a9e200>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d02b6a9e290>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d02b6aa8940>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 16384, "_total_timesteps": 10000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713738888307852999, "learning_rate": 0.0003, "tensorboard_log": null, "_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": -0.6384000000000001, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHwGM6f95yEL+MAWyUS0qMAXSUR0BP7HbAUL2IdX2UKGgGR8BP1RDTjNpuaAdLTGgIR0BP7zlLeyiVdX2UKGgGR8BdsugpSaVlaAdLVWgIR0BP8qYZ2pyZdX2UKGgGR8BSa+Zw4sEraAdLPmgIR0BP9znA6+36dX2UKGgGR8BlzS00FbFCaAdLUGgIR0BP/CTEBKcvdX2UKGgGR8BUEJw4sEq2aAdLTGgIR0BP/F6Z6UqydX2UKGgGR8B2mE9FF2FGaAdLTmgIR0BQAD4593KTdX2UKGgGR8BQNILCvX9SaAdLQGgIR0BQAwAuIyj6dX2UKGgGR8Bg4UDU3GXHaAdLQWgIR0BQBA3xWkrPdX2UKGgGR8AxNYk3S8aoaAdLS2gIR0BQBKLCN0eVdX2UKGgGR8BeDsSwnpjdaAdLY2gIR0BQBWLP2PDHdX2UKGgGR8BoGTXYlIEsaAdLUWgIR0BQBpQtSQ5ndX2UKGgGR8BuhUa0hNdraAdLXWgIR0BQByXt0FKTdX2UKGgGR8BSf9+9alk6aAdLOmgIR0BQCFYlpoK2dX2UKGgGR8BmgxNATqSpaAdLcGgIR0BQB9jgAIY4dX2UKGgGR8A00NPP9kz5aAdLc2gIR0BQDSQT238XdX2UKGgGR8BrFtzp5eJIaAdLTGgIR0BQD3TRYzSDdX2UKGgGR8AbXfhuO0b+aAdLSGgIR0BQEAVTJhfCdX2UKGgGR8Bg+b9OymhuaAdLRmgIR0BQEs5jpcHGdX2UKGgGR0A0Mw4KhL5AaAdLXWgIR0BQEtKAavRrdX2UKGgGR8Badmqo60Y1aAdLPWgIR0BQFSFfzBhydX2UKGgGR8B4yngXMyJsaAdLVWgIR0BQFuxbB42TdX2UKGgGR8B5rRm8M/hVaAdLXWgIR0BQF5Ig/1QJdX2UKGgGR8BVYgRK6FufaAdLUmgIR0BQF+B6KLsKdX2UKGgGR8BAdEC/47A+aAdLTGgIR0BQGgHAymALdX2UKGgGR8B8tl7ngYP5aAdLWWgIR0BQH7y1/lQudX2UKGgGR8Bw4fKMefZmaAdLWGgIR0BQIK02LpA2dX2UKGgGR8BbS/keZG8VaAdLamgIR0BQILFGXokidX2UKGgGR8Bdbpq/M4cWaAdLamgIR0BQItfb9If9dX2UKGgGR8BEAQVTJhfCaAdLa2gIR0BQJEOEug6EdX2UKGgGR8BybFFocrAhaAdLZWgIR0BQI91EE1VHdX2UKGgGR8BdoAAMlTm5aAdLT2gIR0BQKIqPOpsHdX2UKGgGR8Bru3RRdhRZaAdLYmgIR0BQKn752yLRdX2UKGgGR8BTvqaCtihGaAdLbGgIR0BQKyB5HEuQdX2UKGgGR8Bi2KB9Tgl4aAdLQmgIR0BQLD8+A3DOdX2UKGgGR8BkdxU1hsqKaAdLYGgIR0BQLVQyhzvJdX2UKGgGR8BPDWIwdsBRaAdLUGgIR0BQLbdnCfpVdX2UKGgGR8BopQYcebNKaAdLcGgIR0BQLr9hqj8DdX2UKGgGR8BgI00BOpKjaAdLXWgIR0BQMICIUJv6dX2UKGgGR8B/YXKQq7ROaAdLc2gIR0BQNLvb48EFdX2UKGgGR8BYjM7QswtbaAdLbGgIR0BQNZOrQw9JdX2UKGgGR8BhUeY+jdpJaAdLQmgIR0BQNjUNKAavdX2UKGgGR8B1l4wM6RyPaAdLXmgIR0BQOt7F85S4dX2UKGgGR8BVcGY8dPtVaAdLPmgIR0BQO9VaOgg6dX2UKGgGR8BzuQgeRxLkaAdLWmgIR0BQPDJU5uIidX2UKGgGR8BqWGruIAOsaAdLWWgIR0BQPaUmlZX/dX2UKGgGR8BgVTeEZiuuaAdLaWgIR0BQPhUzbeuWdX2UKGgGR8BbL1AeJYT1aAdLUWgIR0BQQIuf29L6dX2UKGgGR8BT2tFBppN9aAdLcGgIR0BQQU78vVVhdX2UKGgGR8BwgQtg8bJfaAdLTmgIR0BQQ0IomXw9dX2UKGgGR0AtbC66J66baAdLUmgIR0BQQ0W69TP0dX2UKGgGR8BgcuRoysS1aAdLVGgIR0BQRj+JgsshdX2UKGgGR8Bc6/1pTMq0aAdLQ2gIR0BQSRmTTvy9dX2UKGgGR8Bh4r4pMHryaAdLXWgIR0BQShArxy4ndX2UKGgGR8B+aq7f51vEaAdLZmgIR0BQSyfxtpEhdX2UKGgGR8BUcI+Sr5qNaAdLRmgIR0BQSy/fwZwXdX2UKGgGR8ByrNC6Ymb9aAdLWWgIR0BQUFK02LpBdX2UKGgGR8BXM8JpnHvMaAdLVmgIR0BQVTbN8ma6dX2UKGgGR8BwinnfVI7OaAdLVWgIR0BQVeKbayrxdX2UKGgGR8BWCK5kK/mDaAdLSWgIR0BQV0Ltu1nedX2UKGgGR8B0xgKsuFpPaAdLgmgIR0BQWBCY1He8dX2UKGgGR8BUyJCSidrgaAdLPmgIR0BQWY46wMYudX2UKGgGR8B4lER5C4SZaAdLXmgIR0BQWW+GoJiRdX2UKGgGR8BZHPykKu0UaAdLXGgIR0BQWipm29csdX2UKGgGR8ByJG3H7xd6aAdLX2gIR0BQYAZGax5cdX2UKGgGR8BTJqV6eGwiaAdLQmgIR0BQX8w1zhgmdX2UKGgGR8BpQy4FzMibaAdLb2gIR0BQYgKneiztdX2UKGgGR8BagmMfigkDaAdLeGgIR0BQYm6bvw3HdX2UKGgGR8BdyRISUTtcaAdLUWgIR0BQYmTTvy9VdX2UKGgGR8BO5Gjbi6xxaAdLYGgIR0BQaNitq59WdX2UKGgGR8BtPtdTo+wDaAdLa2gIR0BQax2jfvWpdX2UKGgGR8Bg533lCCz1aAdLSmgIR0BQbDq8lHBldX2UKGgGR8A/QWJJoTPCaAdLlGgIR0BQb+0gKWszdX2UKGgGR8Bt1E9+w1R+aAdLTmgIR0BQcQlOXVsldX2UKGgGR8B3X0OrhisoaAdLbmgIR0BQcmBOHnEEdX2UKGgGR8BsPltl7MPjaAdLW2gIR0BQdOzQeFL4dX2UKGgGR8BygKIuXeFdaAdLYGgIR0BQdT6rNnoQdX2UKGgGR8BtmTfgrH2iaAdLZWgIR0BQdhYFJQLvdX2UKGgGR8BL99APd2xIaAdLRGgIR0BQdtayKNyYdX2UKGgGR8B+mYqur6tUaAdLbmgIR0BQd8g+yJKrdX2UKGgGR8Bg88M5OrQxaAdLVWgIR0BQe66z3RG+dX2UKGgGR8BoOQla8pTdaAdLR2gIR0BQfi3ocJdCdX2UKGgGR8BsGKZUkv9MaAdLY2gIR0BQgNRm9QGfdX2UKGgGR8Balq6reZXuaAdLbGgIR0BQgW9g4OtodX2UKGgGR8BTNDjvNNahaAdLO2gIR0BQgoPCl7+ldX2UKGgGR8Bvyo5vLowFaAdLhWgIR0BQgsHbAUL2dX2UKGgGR8BSF7nxJ/XoaAdLQ2gIR0BQhcDr7fpEdX2UKGgGR8BkObWVeKKpaAdLe2gIR0BQhWbkOqecdX2UKGgGR8BslMF+uvECaAdLWGgIR0BQhhusLfDUdX2UKGgGR8B8K7/LkjoqaAdLX2gIR0BQiPZyuIRAdX2UKGgGR8Bq37V6NVBEaAdLSmgIR0BQi1iSaEzwdX2UKGgGR8Bju94RmK64aAdLU2gIR0BQjaFAVwgldX2UKGgGR8B8S7jFQ2uQaAdLY2gIR0BQj9xVAAyVdX2UKGgGR8B2scppeu3daAdLXWgIR0BQkbA1vVEvdX2UKGgGR8Bfe0FwDNhWaAdLYWgIR0BQlMjRlYlqdX2UKGgGR8BZUwfEGZ/kaAdLZWgIR0BQlQFotcv/dX2UKGgGR8BqJciILw4LaAdLRWgIR0BQlZqASWZ7dX2UKGgGR8BiZ2RJVbRnaAdLRWgIR0BQlxgiNbTudX2UKGgGR8BgfojSofjkaAdLSWgIR0BQmBOclPaddX2UKGgGR8Bv0AdyT6i1aAdLX2gIR0BQmsF2V3UydWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 4, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "False", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf051890d081c040840593ef4b21f77f72c139a2867cf61038236842733ac76e
+size 147433

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 0x7d02b6a9dc60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d02b6a9dcf0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d02b6a9dd80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d02b6a9de10>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d02b6a9dea0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d02b6a9df30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d02b6a9dfc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d02b6a9e050>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d02b6a9e0e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d02b6a9e170>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d02b6a9e200>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d02b6a9e290>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d02b6aa8940>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 16384,
+    "_total_timesteps": 10000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1713738888307852999,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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": -0.6384000000000001,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 4,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c248220e66eab7082af650b7e0a372d7468fb269327f103ba7b397cb455883a9
+size 87978

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c91bd5cf5f989b2212c861a7f76c1b3b042e0e0ff36247bc6425f0450a571d39
+size 43634

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+cu121
+- GPU Enabled: False
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 248.4883157158353, "std_reward": 23.28492142123289, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-16T23:15:27.853116"}
+{"mean_reward": -196.7426204217598, "std_reward": 197.51867232500553, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-21T23:22:35.160726"}