Patrick A. Winkel
ABSTRACT
The AbstractSwarm Framework [1] was developed to study multi-agent simulation for optimizing logistics scenarios, with a special focus on (but not restricted to) hospital logistics. The basics of a solution to the accompanying competition [2] and the included benchmark problems is to be presented in this paper. To achieve this, Q-Learning [5, 7, 8] is used to determine scores for possible actions an agent can take depending on the state of the AbstractSwarm Graph representing the environment. Every action receives multiple such scores which are in turn aggregated, hence the name "QPlus".
- Daan Apeldoorn. [n. d.]. AbstractSwarm - A Generic Graphical Modeling Language for Multi-Agent Systems. In Multiagent System Technologies. Vol. 8076. Springer Berlin Heidelberg, 180--192. Google Scholar
Cross Ref
- Daan Apeldoorn, Alexander Dockhorn, Lars Hadidi, and Torsten Panholzer. [n. d.]. AbstractSwarm Competition. https://abstractswarm.gitlab.io/abstractswarm_competition/ Accessed 2022-04-09.Google Scholar
- T. Kohonen. [n. d.]. The self-organizing map. 78, 9 ([n. d.]), 1464--1480. Google ScholarCross Ref
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. [n. d.]. Human-level control through deep reinforcement learning. 518, 7540 ([n. d.]), 529--533. Google ScholarCross Ref
- Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement learning: An introduction. MIT press.Google Scholar
- Gerald Tesauro. [n. d.]. Extending Q-learning to general adaptive multi-agent systems. 16 ([n. d.]).Google Scholar
- C.J. C.H. Watkins. [n. d.]. Learning from delayed rewards. https://www.academia.edu/3294050/Learning_from_delayed_rewardsGoogle Scholar
- Christopher J. C. H. Watkins and Peter Dayan. [n. d.]. Q-learning. 8, 3--4 ([n. d.]), 279--292. Google ScholarDigital Library
Index Terms
- AbstractSwarm multi-agent logistics competition entry: QPlus
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