Brain-Inspired Emergence of Behaviors Based on the Desire for Existence by Reinforcement Learning

Abstract

To develop truly autonomous mobile robots, we proposed to introduce internal rewards such as the desire for existence, specific curiosity, diversive curiosity, boredom, and novelty into reinforcement learning. They are expected to make mobile robots capable of behaving appropriately without being told what to do. Firstly, we proposed to use multiple sources of rewards to endow mobile robots with ability to behave properly in the real world. Secondly, we proposed task-independent internal rewards. Thirdly, we proposed to attain engineering merit of internal rewards in addition to scientific interest. A pursuit-evasion game comprising a predator and its prey on a robotic field was selected as a testbed to demonstrate the effectiveness of internal rewards in reinforcement learning. The present paper focuses on learning of pursuit timing to maximize accumulated future rewards by Q-learning and SARSA.