Multiple-Timescale PIA for Model-Based Reinforcement Learning
Tomohiro Yamaguchi* and Eri Imatani**
*Nara National Collage of Technology, 22 Yata-cho, Yamatokoriyama, Nara 639-1080, Japan
**Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
This paper discusses dynamic-programming-based multiagent reinforcement learning in the MDP model. To learn cooperative actions among agents, a major difficulty in multiagent reinforcement learning is the problem of simultaneous learning. To solve this problem, each agent should learn in different time. We propose multiple-timescale reinforcement learning improving their learning results exclusively. We conducted comparative experiments between multiple-timescale and exclusive policy improvement, reducing optimal common-payoff Nash solution search cost.
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