A robust approach to robot team learning

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• 作者：Justin Girard ; M. Reza Emami
• 关键词：Robot team learning ; Markov decision process ; State uncertainty ; Particle filters
• 刊名：Autonomous Robots
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：40
• 期：8
• 页码：1441-1457
• 全文大小：2,176 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Automation and Robotics
  Electronic and Computer Engineering
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Mechanical Engineering
  Simulation and Modeling
  
• 出版者：Springer Netherlands
• ISSN：1573-7527
• 卷排序：40

文摘

The paper achieves two outcomes. First, it summarizes previous work on concurrent Markov decision processes (CMDPs) currently demonstrated for use with multi-agent foraging problems. When using CMDPs, each agent models the environment using two Markov decision process (MDP). The two MDPs characterize a multi-agent foraging problem by modeling both a single-agent foraging problem, and multi-agent task allocation problem, for each agent. Second, the paper studies the effects of state uncertainty on a heterogeneous robot team that utilizes the aforementioned CMDP modelling approach. Furthermore, the paper presents a method to maintain performance despite state uncertainty. The resulting robust concurrent individual and social learning (RCISL) mechanism leads to an enhanced team learning behaviour despite state uncertainty. The paper analyzes the performance of the concurrent individual and social learning mechanism with and without a particle filter for a heterogeneous foraging scenario. The RCISL mechanism confers statistically significant performance improvements over the CISL mechanism.