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Tham khảo tài liệu 'multi-robot systems trends and development 2010 part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 352 Multi-Robot Systems Trends and Development learning steps a Fig. 7 a . Minmax-Q algorithm with the traditional reinforcement function b . Minmax-Q algorithm with the knowledge-base reinforcement function knowledge-base reinforcement function. Obviously we can observe that learning with traditional reinforcement function has worse convergence and still has many unstable factors at end of experiment while the learning with knowledge-base reinforcement function converges rapidly and it gets to stable value about half time of experiment. Therefore with the external knowledge environment information and internal knowledge action effect information multi-agent learning has better performance and effectivity. 3.4 Summary When Multi-agent learning is applied to real environment it is very important to design the reinforcement function that is appropriate to environment and learner. We think that the learning agent must take advantage of the information including environment and itself domain knowledge to integrate the comprehensive reinforcement information. This paper presents the reinforcement function based on knowledge with which the learner not only pays more attention to environment transition but also evaluates its action performance each step. Therefore the reinforcement information of multi-agent learning becomes more abundant and comprehensive so that the leaning can converge rapidly and become more stable. From experiment it is obviously that multi-agent learning with knowledge-base reinforcement function has better performance than traditional reinforcement. However we should point out how to design the reinforcement must depend on the application background of multi-agent learning system. Different task different action effect and different environments are the key factors to influence multi-agent learning. Hence differ from traditional reinforcement function the reinforcement function is build by the characteristic based on real environment and learner .
