Evolutionary computation on multitask reinforcement learning problems

Recently, Multitask learning, which can cope with several tasks, has attracted much attention. Multitask Reinforcement Learning introduced by Tanaka et al is a problem class where number of problem instances of Markov Decision Processes sampled from the same probability distributions is sequentially given to reinforcement learning agents. The purpose of solving this problem is to realize adaptive agents for newly given environments by using knowledge acquired from past experience. Evolutionary Algorithms are often used to solve reinforcement learning problems if problem classes are quite different with Markov Decision Processes or state-action space is quite huge. From the viewpoint of Evolutionary Algorithms studies, the Multitask Reinforcement Learning problems are regarded as dynamic problems whose fitness landscape has changed temporally. In this paper, a memory-based Evolutionary Programming which is suitable for Multitask Reinforcement Learning problems is proposed.