Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1978
Title: An Approach to Multi-Agent Pursuit Evasion Games Using Reinforcement Learning
Authors: Bilgin, Ahmet Tunç
Kadıoğlu-Ürtiş, Esra
143698
Keywords: Reinforcement learning
Watkins's Q(lambda)-learning
Pursuit evasion
Multi-agent systems
Issue Date: 2015
Publisher: IEEE
Source: Bilgin, A. T., & Kadioglu-Urtis, E. (2015, July). An approach to multi-agent pursuit evasion games using reinforcement learning. In 2015 International Conference on Advanced Robotics (ICAR)(pp. 164-169). IEEE.
Abstract: The game of pursuit-evasion has always been a popular research subject in the field of robotics. Reinforcement learning, which employs an agent's interaction with the environment, is a method widely used in pursuit-evasion domain. In this paper, a research is conducted on multi-agent pursuit-evasion problem using reinforcement learning and the experimental results are shown. The intelligent agents use Watkins's Q(lambda)-learning algorithm to learn from their interactions. Q-learning is an off-policy temporal difference control algorithm. The method we utilize on the other hand, is a unified version of Q-learning and eligibility traces. It uses backup information until the first occurrence of an exploration. In our work, concurrent learning is adopted for the pursuit team. In this approach, each member of the team has got its own action-value function and updates its information space independently.
Description: 17th International Conference on Advanced Robotics (2015 : Istanbul; Turkey)
URI: https://ieeexplore.ieee.org/document/7251450
https://hdl.handle.net/20.500.11851/1978
ISBN: 978-1-4673-7509-2
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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