Study of Cooperation Strategy of Robot Based on Parallel Q-Learning Algorithm
How to solve MR (Multi-Robots) in a dynamic environment of the study of knowledge, and to complete a task or solve a problem, the robot can have the same goal , also different goals. Therefore, to put forward two architectures, which are more suitable for MR studying, according to the architecture, to design the improved learning methods algorithm Q for MR, which solve the problems of coordination and cooperation, such as the credit distribution, distribution of resources, tasks and conflict resolution. MR may be learning in independent environment, and fusing results after learning cycle, and the final results is going to be shared by all the robots, and as the basis of reference passing into next learning cycle, increase learning chances between MR and environment. Simulation results show that the learning algorithm enables MR learning rapidly and quickly surrounded by a mobile group, complying with better effective.
KeywordsMulti-Robots Reinforcement Learning Q-learning Dynamic Programming Parallel learning
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- 5.Cai, Y., Chen, J., Yao, J., Li, S.: Global Planning from Local Eyeshot: An Implementation of Observation-based Plan Coordination in Robot Cup Simulation Games. In: Andreas, B., Silvia, C., Satoshi, T. (eds.) RobotCup 2001: Robot Soccer World Cup V. Springer, Heidelberg (2002)Google Scholar
- 6.Asama, H., Matsumoto, A., Ishida, Y.: Design of an Autonomous and Distributed Robot System. In: Proceedings of the IEEE/RSJ International Workshop on the Intelligent Robots and System, Tsukuba, pp. 283–290 (1989)Google Scholar
- 8.Wang, S., Zhu, Q., Lui, Z., Yang, J., Si, F.: Study of Reinforcement Learning Based on Multi-agent Robot Systems. J. Computational Information Systems. 3, 2001–2006 (2007)Google Scholar