Accumulator Based Arbitration Model for both Supervised and Reinforcement Learning Inspired by Prefrontal Cortex

Masahiko Osawa, Yuta Ashihara, Takuma Seno, Michita Imai, Satoshi Kurihara

Abstract: A method that provides an excellent performance by arbitrating multiple modules is important. There are variety of multi-module arbitration methods proposed in various contexts. However, there is yet to be a multi-module arbitration method proposed in reference to structure of animals’ brains. Considering that the animals’ brains achieve general-purpose multi-module arbitration, such function may be achieved by referring to the actual brain. In this paper, with reference to the knowledge of accumulator neurons hypothesized to exist in the prefrontal cortex, we propose an Accumulator Based Arbitration Model (ABAM). By arbitrating multiple modules, ABAM exerts a superior performance in both supervised learning and reinforcement learning task.