Game Theory-Based Control System Algorithms with Real-Time Reinforcement Learning: How to Solve Multiplayer Games Online
Abstract
Complex human-engineered systems involve an interconnection of multiple decision makers (or agents) whose collective behavior depends on a compilation of local decisions that are based on partial information about each other and the state of the environment [1]-[4]. Strategic interactions among agents in these systems can be modeled as a multiplayer simultaneous-move game [5]-[8]. The agents involved can have conflicting objectives, and it is natural to make decisions based upon optimizing individual payoffs or costs.
Recommended Citation
K. G. Vamvoudakis et al., "Game Theory-Based Control System Algorithms with Real-Time Reinforcement Learning: How to Solve Multiplayer Games Online," IEEE Control Systems, vol. 37, no. 1, pp. 33 - 52, Institute of Electrical and Electronics Engineers (IEEE), Feb 2017.
The definitive version is available at https://doi.org/10.1109/MCS.2016.2621461
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Decision Making; E-Learning; Reinforcement Learning; Collective Behavior; Conflicting Objectives; Decision Makers; Engineered Systems; Multiplayer Games; Partial Information; Strategic Interactions; System Algorithm; Game Theory
International Standard Serial Number (ISSN)
1066-033X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Feb 2017
