  Deep Reinforcement Learning (DRL) methods often rely on the meticulous tuning
of hyperparameters to successfully resolve problems. One of the most
influential parameters in optimization procedures based on stochastic gradient
descent (SGD) is the learning rate. We investigate cyclical learning and
propose a method for defining a general cyclical learning rate for various DRL
problems. In this paper we present a method for cyclical learning applied to
complex DRL problems. Our experiments show that, utilizing cyclical learning
achieves similar or even better results than highly tuned fixed learning rates.
This paper presents the first application of cyclical learning rates in DRL
settings and is a step towards overcoming manual hyperparameter tuning.
