Goal Evolution based on Adaptive Q-learning for Intelligent Agent

摘要

This paper presents an adaptive approach to address the goal evolution of the intelligent agent. When agents are initially created, they have some goals and few capabilities. These capabilities can perform some actions to satisfy their goals. They strive to adapt themselves to the low capabilities. Reinforcement learning method is used to the evolution of agent goal. An abstract agent programming language (3APL) is introduced to build the agent mental states. We propose reinforcement learning to refine the top-level goals. A robot soccer game is used to explain our approach. Moreover, we show how a refinement of the soccer player's mental state is derived from the evolving goals by reinforcement learning.