Autonomous Strategy Determination with Learning of Environments in Multi-agent Continuous Cleaning

Ayumi Sugiyama, Toshiharu Sugawara

doi:10.1007/978-3-319-13191-7_36

Autonomous Strategy Determination with Learning of Environments in Multi-agent Continuous Cleaning

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説明

With the development of robot technology, we can expect self- propelled robots working in large areas where coordinated and collaborative behaviors by multiple robots are necessary. Thus, the learning appropriate strategy for coordination and cooperation in multiple autonomous agents is an important issue. However, conventional methods assumed that agents was given knowledge about the environment. This paper proposes a method of autonomous strategy learning for multiple agents coordination integrated with learning where are easy to become dirty in the environments using examples of continuous cleaning tasks. We found that agents with the proposed method could operate as effectively as those with the conventional method and we found that the proposed method often outperformed it in complex areas by splitting up in their works.