Abstract
This paper proposes a new reinforcement learning approach for acquiring conflict avoidance behaviors in multi-agent systems. Multi-agent systems are able to construct orderly systems autonomously through interactions with autonomous agents. We expect to be able to construct flexible and robust systems for the environmental changes by multi-agent system approaches. However, it is difficult for designers to preliminarily embed appropriate behaviors to avoid conflict because complex dynamics emerge by interactions with many agents. We demonstrate the effectivity of the proposed method in narrow road problems that many agents go by each other in narrow roads. In narrow road problems, it is the optimal strategy that agents select behaviors different from other agents. However, agents are faced with a dilemma because they cannot predict other agents' behaviors beforehand. The proposed method can differentiate into agents preferring to pass through a road and agents preferring to give way, by Q-learning that can adjust discount rates. We show that agents differentiate into two type of agents, and acquire stable conflict avoidance behaviors with higher probability than conventional Q-learning algorithms, through experimental results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.