Evolution of meta-parameters in reinforcement learning algorithm

A crucial issue in reinforcement learning applications is how to set meta-parameters, such as the learning rate and "temperature" for exploration, to match the demands of the task and the environment. In this paper, we propose a method to adjust meta-parameters of reinforcement learning by real-number genetic algorithm. It was shown in simulations of foraging tasks that appropriate settings of meta-parameters, which are strongly dependent on each other, can be found by evolution. Furthermore, we verified in hardware experiments using cyber rodent (CR) robots that the meta-parameters evolved in simulation are helpful for learning in real hardware.