Effective Action Learning Method Using Information Entropy for a Single Robot Under Multi-Agent Control

Abstract

Robots that learn to perform actions using reinforcement learning to should be able to learn not only static environments, but also environmental changes. Heterogeneous multi-agent reinforcement learning (HMARL) was developed to perform an efficient search, with multiple agents mounted on a single robot to achieve tasks quickly. Responding to environmental changes using normal reinforcement learning can be challenging. However, HMARL does not consider the use of multiple agents to address environmental changes. In this study, we filtered the agents in HMARL using information entropy to realize a robot capable of maintaining high task achievement rates in response to environmental changes.