By the same authors

Plan-based reward shaping for multi-agent reinforcement learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationProceedings of the Adaptive and Learning Agents Workshop 2012, ALA 2012 - Held in Conjunction with the 11th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012
DatePublished - 2012
Pages49-56
Number of pages8
Original languageEnglish

Abstract

Recent theoretical results have justified the use of potentialbased reward shaping as a way to improve the performance of multi-agent reinforcement learning (MARL). However, the question remains of how to generate a useful potential function. Previous research demonstrated the use of STRIPS operator knowledge to automatically generate a potential function for single-agent reinforcement learning. Following up on this work, we investigate the use of STRIPS planning knowledge in the context of MARL. Our results show that a potential function based on joint or individual plan knowledge can significantly improve MARL performance compared with no shaping. In addition, we investigate the limitations of individual plan knowledge as a source of reward shaping in cases where the combination of individual agent plans causes conflict.

    Research areas

  • Reinforcement learning, Reward shaping

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