We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-agent games. We make the observation that in a competitive setting with adaptive...
Pieter Jan't Hoen, Sander M. Bohte, Han La Poutr&e...
Multi-Agent Agreement problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from d...
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, ...
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...
Information retrieval techniques have to face both the growing amount of data to be processed and the "natural" distribution of these data over the network. Hence, we in...