Utility of Turning Spot Learning under complex goal search and the limit of memory usage

Chain Form Reinforcement Learning (CFRL) was proposed for a reinforcement learning agent using low memory. However, we hold unused information in the memory. In this paper, we introduce Turning Spot Learning (TSL). The method allows an agent to learn with less memory than a CFRL agent. TSL is a method which imitates human perceptions. If we are asked direction, we often tell spot where we changes our action. We call it "Turning Spot". It retains information regarding state, action and distance of a present spot to a next spot. A TSL agent learns only Turning Spots and uses our original action selection method using nearest neighbor algorithm. And, we attempted to limit the amount of the memory usage that a TSL usage can use. Our method was made a comparison to Q-Learning and CFRL in two kinds of goal search problems. We examined performance and discussed the best usage environment.