Abstract. Dyna is a single-agent architectural framework that integrates learning, planning, and reacting. Well known instantiations of Dyna are Dyna-AC and Dyna-Q. Here a multiage...
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
Today's society is largely connected and many real life applications lend themselves to be modeled as multi-agent systems. Although such systems as well as their models are d...
We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the ...
In Multi-Agent learning, agents must learn to select actions that maximize their utility given the action choices of the other agents. Cooperative Coevolution offers a way to evol...