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    "ac_kwargs":	{
        "activation":	"ReLU",
        "hidden_sizes":	[
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    "act_noise":	0.1,
    "actor_critic":	"MLPActorCritic",
    "batch_size":	100,
    "env_fn":	"functools.partial(functools.partial(functools.partial(functools.partial(functools.partial(functools.partial(functools.partial(functools.partial(functools.partial(<function env_fn at 0x7f76de14bea0>, env_name='gyroscopeenv-v0', reward_type='Quadratic', reward_args={'qx1': 9, 'qx2': 0.05, 'qx3': 9, 'qx4': 0.05, 'pu1': 0.1, 'pu2': 0.1}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Absolute', reward_args={'qx1': 9, 'qx2': 0.05, 'qx3': 9, 'qx4': 0.05, 'pu1': 0.5, 'pu2': 0.5}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Normalized', reward_args={'k': 0.1, 'pu1': 0.25, 'pu2': 0.25}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Quadratic with ending penalty', reward_args={'qx1': 9, 'qx2': 0.05, 'qx3': 9, 'qx4': 0.05, 'pu1': 0.1, 'pu2': 0.1, 'sx1': 100, 'sx3': 100, 'end_horizon': 1}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Quadratic with penalty', reward_args={'qx1': 9, 'qx2': 0.05, 'qx3': 9, 'qx4': 0.05, 'pu1': 0.25, 'pu2': 0.25, 'bound': 0.2, 'penalty': 40}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Quadratic with penalty', reward_args={'qx1': 9, 'qx2': 0.05, 'qx3': 9, 'qx4': 0.05, 'pu1': 0.25, 'pu2': 0.25, 'bound': 0.2, 'penalty': 40}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Quadratic with exponential', reward_args={'qx1': 1, 'qx2': 0, 'qx3': 1, 'qx4': 0, 'pu1': 0, 'pu2': 0, 'eax1': 10, 'ebx1': 10, 'eax3': 10, 'ebx3': 10}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Quadratic with penalty', reward_args={'qx1': 9, 'qx2': 0.05, 'qx3': 9, 'qx4': 0.05, 'pu1': 0.25, 'pu2': 0.25, 'bound': 0.2, 'penalty': 40}, ep_len=110), env_name='gyroscopeenv-v0', reward_type='Quadratic with bonus', reward_args={'qx1': 1, 'qx2': 0, 'qx3': 1, 'qx4': 0, 'pu1': 0, 'pu2': 0, 'bound': 0.05, 'bonus': 2}, ep_len=110)",
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    "gamma":	0.995,
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            "first_row":	true,
            "log_current_row":	{},
            "log_headers":	[],
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    "pi_lr":	0.0025,
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    "q_lr":	0.0025,
    "replay_size":	1000000,
    "save_freq":	1,
    "seed":	0,
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    "steps_per_epoch":	1650,
    "update_after":	1000,
    "update_every":	50
}