A Promoting Method of Role Differentiation Using a Learning Rate that Has a Periodically Negative Value in Multi-agent Reinforcement Learning

DOI DOI Open Access

Description

There have been many studies on multi-agent reinforcement learning (MARL) in which each autonomous agent obtains its own control rule by RL. Here, we hypothesize that different agents having individuality is more effective than uniform agents in terms of role differentiation in MARL. In this paper, we propose a promoting method of role differentiation using a wave-form changing parameter in MARL. Then we confirm the effectiveness of role differentiation by the learning rate that has a periodically negative value through computational experiments.

Journal

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top