Improve Convergence Speed of Multi‐Agent Q‐Learning for Cooperative Task Planning

This chapter aims at extending traditional multi‐agent Q‐learning (MAQL) algorithms to improve their speed of convergence by incorporating two interesting properties, concerning exploration of the team‐goal and selection of joint action at a given joint state. It begins by reviewing the preliminaries of reinforcement leaning (RL). The motivation of RL is to derive the optimal action at a given environmental state for which the agent would be able to derive the maximum reward. Such formulation of deriving optimal action at a given state based on the learned experience of interaction with the environment has plenty of interesting applications, including generating moves in a game, and complex task‐planning and motion‐planning of a mobile robot in a constrained environment. The chapter then introduces the proposed fast cooperative multi‐agent Q‐learning algorithms. The chapter deals with multi‐agent cooperative planning algorithms and includes experiments and results.