finetunning some hyperparameters

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 262.59 +/- 14.26
+      value: -52.64 +/- 21.59
       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 0x7f5cd150e9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5cd150ea60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5cd150eaf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5cd150eb80>", "_build": "<function ActorCriticPolicy._build at 0x7f5cd150ec10>", "forward": "<function ActorCriticPolicy.forward at 0x7f5cd150eca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5cd150ed30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5cd150edc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5cd150ee50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5cd150eee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5cd150ef70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5cd150a720>"}, "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": 1670424010565570947, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 276, "n_steps": 1024, "gamma": 0.998, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "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 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 0x7fa49edb74c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa49edb7550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa49edb75e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa49edb7670>", "_build": "<function ActorCriticPolicy._build at 0x7fa49edb7700>", "forward": "<function ActorCriticPolicy.forward at 0x7fa49edb7790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa49edb7820>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa49edb78b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa49edb7940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa49edb79d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa49edb7a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa49edada80>"}, "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": 1670586373173339233, "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:": "gAWVgRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMInKiluZUPZkCUhpRSlIwBbJRN2QOMAXSUR0CZI4XNke6qdX2UKGgGaAloD0MIqYk+H2VIX8CUhpRSlGgVTQEDaBZHQJkoade6Zpl1fZQoaAZoCWgPQwiYTBWMSvpCQJSGlFKUaBVN6ANoFkdAmS5aFIuoP3V9lChoBmgJaA9DCLB0PjxLMBTAlIaUUpRoFU3oA2gWR0CZLwxAjY7JdX2UKGgGaAloD0MIRMTNqWTAL8CUhpRSlGgVTegDaBZHQJkxMyTINmV1fZQoaAZoCWgPQwi2gqYlVo5fwJSGlFKUaBVNOQNoFkdAmTMcophF3XV9lChoBmgJaA9DCFMDzefc1UPAlIaUUpRoFU3oA2gWR0CZUyYeT3ZgdX2UKGgGaAloD0MI9mBSfHzTakCUhpRSlGgVTaMCaBZHQJlUXCZWq951fZQoaAZoCWgPQwjS5c3hWvtHwJSGlFKUaBVN6ANoFkdAmVniG34KyHV9lChoBmgJaA9DCCGunL0zbFLAlIaUUpRoFU1XAmgWR0CZWjTqSowVdX2UKGgGaAloD0MIB+3Vx8OcZ8CUhpRSlGgVTbkDaBZHQJlahk1/DtR1fZQoaAZoCWgPQwg7HF2lu98zwJSGlFKUaBVNIQJoFkdAmWelOGj9GnV9lChoBmgJaA9DCHmRCfg1jE/AlIaUUpRoFU3oA2gWR0CZaiLtNSIhdX2UKGgGaAloD0MIM/59xoUmV0CUhpRSlGgVTbEDaBZHQJlqPf1pTMt1fZQoaAZoCWgPQwhsCmR2FnJhwJSGlFKUaBVNIwNoFkdAmXQOFxn3+XV9lChoBmgJaA9DCPsjDAMW5mZAlIaUUpRoFU2gAmgWR0CZfVHPNVzZdX2UKGgGaAloD0MI6SgHswm9VkCUhpRSlGgVTfECaBZHQJmE0Ttb9qF1fZQoaAZoCWgPQwjW/znMl6RYwJSGlFKUaBVNDwNoFkdAmYtpyZKFqXV9lChoBmgJaA9DCDuOHyqN2Py/lIaUUpRoFU3oA2gWR0CZjaHj6vaDdX2UKGgGaAloD0MIoS3nUtwrYkCUhpRSlGgVTc8DaBZHQJmO7779AHF1fZQoaAZoCWgPQwg1KQXd3r5kQJSGlFKUaBVNygJoFkdAmY8FoHs1K3V9lChoBmgJaA9DCIjxmld1VkpAlIaUUpRoFU2jA2gWR0CZj7tNzr/sdX2UKGgGaAloD0MIhuXPtwU4WUCUhpRSlGgVTSsDaBZHQJmfDhFVktp1fZQoaAZoCWgPQwgbvK/KhZFTwJSGlFKUaBVN6ANoFkdAmaGRa9sabXV9lChoBmgJaA9DCE88ZwsI5lZAlIaUUpRoFU1KA2gWR0CZomQUHpr2dX2UKGgGaAloD0MIDK1OzlASUECUhpRSlGgVTegDaBZHQJmmqfg75mB1fZQoaAZoCWgPQwhq9dVVAYBiQJSGlFKUaBVNxgJoFkdAmcaWBas6rHV9lChoBmgJaA9DCLQAbatZplZAlIaUUpRoFU0UA2gWR0CZyjECeVcEdX2UKGgGaAloD0MIQKTfvg48L0CUhpRSlGgVTegDaBZHQJnL23+dbxF1fZQoaAZoCWgPQwjaBBiWP41AQJSGlFKUaBVN6ANoFkdAmd6PIsAeaXV9lChoBmgJaA9DCAiPNo5Y+llAlIaUUpRoFU0qA2gWR0CZ6S+x4Y78dX2UKGgGaAloD0MINNb+znaMZECUhpRSlGgVTdQCaBZHQJnq2nm7rcF1fZQoaAZoCWgPQwgAcVevIhNWwJSGlFKUaBVN6ANoFkdAmery5iExqXV9lChoBmgJaA9DCFGIgEOofV7AlIaUUpRoFU3cAmgWR0CZ66cKw6hhdX2UKGgGaAloD0MIoFIlyt6RXECUhpRSlGgVTSEDaBZHQJnuUKMNtqJ1fZQoaAZoCWgPQwiVKHtLudpjwJSGlFKUaBVN8wJoFkdAme5f/echDHV9lChoBmgJaA9DCM3OoncqVETAlIaUUpRoFU3oA2gWR0CZ8vB7/n4gdX2UKGgGaAloD0MIxM4UOi/AZUCUhpRSlGgVTcgCaBZHQJn4awkgOjJ1fZQoaAZoCWgPQwiH+l3YmlVFQJSGlFKUaBVN6ANoFkdAmf/wf+0gKXV9lChoBmgJaA9DCAO0rWadP2NAlIaUUpRoFU3SAmgWR0CaAD9VFQVLdX2UKGgGaAloD0MIJ4Oj5FWeaECUhpRSlGgVTZMCaBZHQJoD+hysCDF1fZQoaAZoCWgPQwh6cHfWbkNOQJSGlFKUaBVNywNoFkdAmhBc+FDfFnV9lChoBmgJaA9DCOfFia92vD7AlIaUUpRoFU3oA2gWR0CaEi4GD+R6dX2UKGgGaAloD0MINGWnH9TPS8CUhpRSlGgVTeoBaBZHQJoTVqEeyRl1fZQoaAZoCWgPQwjrjO+LS+k3QJSGlFKUaBVN6ANoFkdAmjcjtgKF7HV9lChoBmgJaA9DCI/gRsqWcWFAlIaUUpRoFU1yAmgWR0CaOmNcW0qpdX2UKGgGaAloD0MIo5QQrKppVMCUhpRSlGgVTegDaBZHQJo68w0wait1fZQoaAZoCWgPQwjrOlRTkolbQJSGlFKUaBVNygJoFkdAmj/g9Net0XV9lChoBmgJaA9DCDyGx34WLWHAlIaUUpRoFU2jAmgWR0CaQSXL/0dzdX2UKGgGaAloD0MINSVZhyPEZsCUhpRSlGgVTWADaBZHQJpC/euV5bB1fZQoaAZoCWgPQwjwiArVzddfwJSGlFKUaBVNRQNoFkdAmkpM/QjUu3V9lChoBmgJaA9DCG3KFd5lG2RAlIaUUpRoFU3EAmgWR0CaTYO2y9mIdX2UKGgGaAloD0MIL204LA2iZECUhpRSlGgVTb4CaBZHQJpULReC04R1fZQoaAZoCWgPQwj0+SgjLhpYwJSGlFKUaBVNHQJoFkdAmlebcwg1WXV9lChoBmgJaA9DCCrIz0auQ1HAlIaUUpRoFU3oA2gWR0CaWmnNxEORdX2UKGgGaAloD0MIAkpDjULLUkCUhpRSlGgVTTMDaBZHQJpdyrBCUot1fZQoaAZoCWgPQwgJ+3YSEetZwJSGlFKUaBVN6ANoFkdAml7++IuXeHV9lChoBmgJaA9DCCHIQQkzDWZAlIaUUpRoFU15AmgWR0CaZ9DziCJ5dX2UKGgGaAloD0MIRztu+N10s7+UhpRSlGgVTaIBaBZHQJpqr974SHx1fZQoaAZoCWgPQwhVbMzriLs2wJSGlFKUaBVN6ANoFkdAmm/fCEYfn3V9lChoBmgJaA9DCJ4MjpJXr1dAlIaUUpRoFU16A2gWR0CadUOE/SpjdX2UKGgGaAloD0MIdTqQ9VQjZECUhpRSlGgVTfYCaBZHQJp202Jiy6d1fZQoaAZoCWgPQwhjgEQTKN9iQJSGlFKUaBVNswJoFkdAmnnAwwj+rHV9lChoBmgJaA9DCN8bQwBwvERAlIaUUpRoFU3oA2gWR0CafwJGOMl1dX2UKGgGaAloD0MIIHnnUAbPZMCUhpRSlGgVTXADaBZHQJp/mmbb1yx1fZQoaAZoCWgPQwiJ7IMsCwddwJSGlFKUaBVNRQNoFkdAmoGZCF9KEnV9lChoBmgJaA9DCJCfjVy3cmVAlIaUUpRoFU21A2gWR0Cap9dYW+GodX2UKGgGaAloD0MIYAZjRKJmTUCUhpRSlGgVTegDaBZHQJq7tyksSTR1fZQoaAZoCWgPQwhVT+YffRBhwJSGlFKUaBVNOQNoFkdAmsDs4xUNrnV9lChoBmgJaA9DCEOSWb3Dn0NAlIaUUpRoFU3oA2gWR0CaxP2IwdsBdX2UKGgGaAloD0MI2hznNuFmScCUhpRSlGgVTegDaBZHQJrJQDHOryV1fZQoaAZoCWgPQwgJGcizy487QJSGlFKUaBVN6ANoFkdAmszOQdS2pnV9lChoBmgJaA9DCN/5RQn6dmJAlIaUUpRoFU07A2gWR0CazVl3Qla9dX2UKGgGaAloD0MIRgiPNo5uRkCUhpRSlGgVTegDaBZHQJrQo/Vy3kR1fZQoaAZoCWgPQwh9dytL9MdhQJSGlFKUaBVNVgNoFkdAmtf3Zbpu/HV9lChoBmgJaA9DCJyIfm39+mBAlIaUUpRoFU1jA2gWR0Ca3l4pMHrydX2UKGgGaAloD0MIIqtbPae9YECUhpRSlGgVTTIDaBZHQJre6oXKr7x1fZQoaAZoCWgPQwjAQubKoEo9wJSGlFKUaBVN6ANoFkdAmt9kJOWSlnV9lChoBmgJaA9DCMvVj03yCV3AlIaUUpRoFU34AmgWR0Ca36qSX+l1dX2UKGgGaAloD0MIwO0JEltZZUCUhpRSlGgVTQoDaBZHQJrhbadtl7N1fZQoaAZoCWgPQwiOPXsuU1MDQJSGlFKUaBVN6ANoFkdAmujUO/cnE3V9lChoBmgJaA9DCGngRzXsY1FAlIaUUpRoFU3oA2gWR0Ca9FBNmDlHdX2UKGgGaAloD0MIjbYqiWyBY0CUhpRSlGgVTfwCaBZHQJsbAqslsxh1fZQoaAZoCWgPQwi/DTFe8w44QJSGlFKUaBVN6ANoFkdAmx0ihew9q3V9lChoBmgJaA9DCJliDoKOLlpAlIaUUpRoFU1kA2gWR0CbLd+N96TodX2UKGgGaAloD0MIQKGePgLDXkCUhpRSlGgVTS0DaBZHQJswGpDNQj51fZQoaAZoCWgPQwjohqbsdFNjQJSGlFKUaBVNgAJoFkdAmzP5jhDPW3V9lChoBmgJaA9DCBIxJZLoXlJAlIaUUpRoFU3oA2gWR0CbNqxoqTbGdX2UKGgGaAloD0MIO8YVF8dpYkCUhpRSlGgVTe4CaBZHQJs8mofjjrB1fZQoaAZoCWgPQwgR/7ClRyZSwJSGlFKUaBVN6ANoFkdAmz3eC04R3HV9lChoBmgJaA9DCJutvOR/nlhAlIaUUpRoFU3eA2gWR0CbQHtZmqYJdX2UKGgGaAloD0MIzhjmBG3mN8CUhpRSlGgVTegDaBZHQJtEgBS1map1fZQoaAZoCWgPQwj8j0yHTiRlQJSGlFKUaBVNRgNoFkdAm0SvdIoVmHV9lChoBmgJaA9DCD5A9+VM2mRAlIaUUpRoFU0tA2gWR0CbRXHJLdvbdX2UKGgGaAloD0MIOGdEaW/NXsCUhpRSlGgVTaUDaBZHQJtFwoH9m6J1fZQoaAZoCWgPQwiR8/4/zktgwJSGlFKUaBVNSwNoFkdAm06Wc8TzunV9lChoBmgJaA9DCOD2BInt2ExAlIaUUpRoFU3oA2gWR0CbUE3pfQa8dX2UKGgGaAloD0MIeTwtP3CkXMCUhpRSlGgVTdQCaBZHQJtYVz2exwB1fZQoaAZoCWgPQwiaBkXzAAZGQJSGlFKUaBVN6ANoFkdAm2fGnCO3lXV9lChoBmgJaA9DCEq2upwSt1TAlIaUUpRoFU39AWgWR0CbaMvFFUhndWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 372, "n_steps": 1024, "gamma": 0.969, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 6, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+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 sha256:89e7f148994bd2b58e81c73a29841165c8c61735d80cc78bea10706c76621539
-size 147001
+oid sha256:ef66cd0b4fe48645958eaddf3ee79fecdfe6f79d34e2b89ae85bc06b635e3d0a
+size 147218

ppo-LunarLander-v2/data

@@ -4,19 +4,19 @@
         ":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 0x7f826312a1f0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f826312a280>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f826312a310>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f826312a3a0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f826312a430>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f826312a4c0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f826312a550>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f826312a5e0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f826312a670>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f826312a700>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f826312a790>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fa49edb74c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa49edb7550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa49edb75e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa49edb7670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa49edb7700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa49edb7790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa49edb7820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa49edb78b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa49edb7940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa49edb79d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa49edb7a60>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f826311ccf0>"
+        "_abc_impl": "<_abc_data object at 0x7fa49edada80>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,12 +42,12 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 16384,
-    "_total_timesteps": 1000,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1670420281761146293,
+    "start_time": 1670586373173339233,
     "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'>",
@@ -66,24 +66,24 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -15.384,
+    "_current_progress_remaining": -0.015808000000000044,
     "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": 4,
+    "_n_updates": 372,
     "n_steps": 1024,
-    "gamma": 0.999,
+    "gamma": 0.969,
     "gae_lambda": 0.98,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,
-    "n_epochs": 4,
+    "n_epochs": 6,
     "clip_range": {
         ":type:": "<class 'function'>",
         ":serialized:": "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"

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:51a185c873bd1ea53802d32011c9256ab1cfa9b8edd638af106539c6fb81fb53
-size 87865
+oid sha256:245238e8c205fe957a45ca4fe291ac7308a4523871b2a0b2c8d3aad7bbaed71f
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:75d2f52f1b309039e5333dda4b925407d40eb50dba349ad8296ecf3281854ffa
+oid sha256:9d71880623b09a9abd70f7701df4e493cfcbc6dc1f752a79ac55ff82abe6cc66
 size 43201

ppo-LunarLander-v2/system_info.txt

@@ -1,7 +1,7 @@
 OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
-Python: 3.8.15
+Python: 3.8.16
 Stable-Baselines3: 1.6.2
-PyTorch: 1.12.1+cu113
+PyTorch: 1.13.0+cu116
 GPU Enabled: True
 Numpy: 1.21.6
 Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 262.5861671060246, "std_reward": 14.259336501656048, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-07T14:59:23.233664"}
+{"mean_reward": -52.635233656874334, "std_reward": 21.588562825827957, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-09T12:16:28.726117"}