Abstract In this paper we address the problem of simultaneous learning and coordination in multiagent Markov decision problems (MMDPs) with infinite state-spaces. We separate this ...
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...
Abstract— This paper presents a genetic algorithmic approach for finding efficient paths in directed graphs when optimizing multiple objectives. Its aim is to provide solutions...
: Other work has shown that adaptive learning can be highly successful in developing programs which are able to play games at a level similar to human players and, in some cases, e...
We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is relation among those tasks, then the information gained duri...