A Reinforcement Learning Based Multiple Strategy Framework for Tracking a Moving Target

摘要

The pursuit-evasion game has been a classic research topic in the field of mobile robotics. Reinforcement learning (RL), which shows outstanding advantages in the decision-making area, is a widely used method in pursuitevasion game. This paper proposes a hierarchical framework in which RL allows the pursuer to select an appropriate strategy for the current condition in the upper level from multiple underlying strategies in the lower level. Through the analysis of existing motion planning algorithms, the dynamic window approach (DWA) and the proportional guidance method (PG) are used as the lower level strategies of the framework. Individual discussions on the merits and limitations of the two motion planning algorithms indicate a possible complementarity between them. Simulations are carried out and the corresponding results validate the excellent performance of the proposed approach.