Replicator Dynamics for Multi-agent Learning: An Orthogonal Approach Michael Kaisers and Karl Tuyls Maastricht University, P.O. Box 616, 6200 MD Maastricht Abstract. Today’s society is largely connected and many real - Differential game - Document - PDFSEARCH.IO

First Page	Meta Content
	Replicator Dynamics for Multi-agent Learning: An Orthogonal Approach Michael Kaisers and Karl Tuyls Maastricht University, P.O. Box 616, 6200 MD Maastricht Abstract. Today’s society is largely connected and many real Add to Reading List Document Date: 2012-04-29 08:03:37 Open Document File Size: 679,43 KB Share Result on Facebook Facility Karl Tuyls Maastricht University / / IndustryTerm heuristic search / energy eﬃcient / multi-agent systems / dynamical systems / learning algorithm / real life applications / learning algorithms / nd solutions / / Organization Orthogonal Approach Michael Kaisers and Karl Tuyls Maastricht University / / Person Karl Tuyls / / Position actor / player / second player / / Technology learning algorithm / Cross Learning algorithm / One protocol / simulation / Q-learning algorithm / / SocialTag Evolutionary game theory Replicator equation Reinforcement learning Dynamics Dynamical system Differential equation Learning automata Machine learning Evolutionary biology Evolutionary dynamics

Replicator Dynamics for Multi-agent Learning: An Orthogonal Approach Michael Kaisers and Karl Tuyls Maastricht University, P.O. Box 616, 6200 MD Maastricht Abstract. Today’s society is largely connected and many real Add to Reading List