In many Multi-Agent Systems (MAS), agents (even if selfinterested) need to cooperate in order to maximize their own utilities. Most of the multi-agent learning algorithms focus on...
Jose Enrique Munoz de Cote, Alessandro Lazaric, Ma...
Learning in many multi-agent settings is inherently repeated play. This calls into question the naive application of single play Nash equilibria in multi-agent learning and sugges...
The impact of learning on evolution in dynamic environments undergoes recognized stages of the Baldwin Effect although its cause is not clear. To identify it experimentally, we de...
We describe how to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and combine them into a composite strategy. We provide an...
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...