Transition-state replicator dynamics
Document Type
Article
Publication Date
11-15-2021
Abstract
Agent-based evolutionary game theory studies the dynamics of the autonomous agents. It is important for application that relies on the agents to perform the automated tasks. Since the agents make their own decision, therefore the stability of the interaction needs to be comprehended. The current state of the art in agent-based replicator dynamics are piecewise and state-coupled replicator dynamics which focus on joint-action single-state reward. This paper introduces additional reward parameter to the learning algorithm, extends the replicator dynamics to joint-action transition-state reward and shows that it can be changed to single-state reward and independent-action reward. The replicator equation is expressed based on the tree diagram approach and is verified with the numerical simulation in a two states battle of sexes coordination game for various types of rewards. The numerical results are consistent with the phase portraits generated by the replicator equation and are able to provide some general insights to the coordination game such as the number of convergence points, the rate of convergence and the effect of initial points on the convergence.
Keywords
Evolutionary game theory, Multi-agent learning, Replicator dynamics
Divisions
umpedac
Funders
Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia,Universiti Malaya [PG208-2015A],UM-MOHE HIR grant [UM/HIR/MOHE/ENG/22],MyPhD under MyBrain15 of Kementerian Pendidikan Malaysia (KPM),Swinburne University of Technology, Melbourne, Australia,UM Power Energy Dedicated Advanced Centre (UMPEDAC),Higher Institution Centre of Excellence (HICoE) Program Research Grant,Ministry of Education, Malaysia [RU003-2020]
Publication Title
Expert Systems with Applications
Volume
182
Publisher
Elsevier
Publisher Location
THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND