Study of Cooperation Strategy of Robot Based on Parallel Q-Learning Algorithm
- Cite this paper as:
- Wang S., Si F., Yang J., Wang S., Yang J. (2008) Study of Cooperation Strategy of Robot Based on Parallel Q-Learning Algorithm. In: Xiong C., Huang Y., Xiong Y., Liu H. (eds) Intelligent Robotics and Applications. ICIRA 2008. Lecture Notes in Computer Science, vol 5314. Springer, Berlin, Heidelberg
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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