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AAMAS
2006
Springer

An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games

8 years 4 months ago
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov property is not valid. This requires agents to be adaptive. RL is a natural approach to model the learning of individual agents. These Learning algorithms are however known to be sensitive to the correct choice of parameter settings for single agent systems. This issue is more prevalent in the MAS case due to the changing interactions amongst the agents. It is largely an open question for a developer of MAS of how to design the individual agents such that, through learning, the agents as a collective arrive at good solutions. We will show that modeling RL in MAS, by taking an evolutionary game theoretic point of view, is a new and potentially successful way to guide learning agents to the most suitable solution for their task at hand. We show how evolutionary dynamics (ED) from Evolu...
Karl Tuyls, Pieter Jan't Hoen, Bram Vanschoenwinke
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where AAMAS
Authors Karl Tuyls, Pieter Jan't Hoen, Bram Vanschoenwinkel
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