Learning automata: an introduction
Learning automata: an introduction
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
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The analysis of the collective behavior of agents in a distributed multi-agent environment received a lot of attention in the past decade. More accurately, coordination was studied intensely because it enables agents to converge to Pareto optimal solutions and Nash equilibria. Most of these studies focussed on team games. In this paper we report on a technique for finding fair solutions in conflicting interest multi-stage games. Our hierarchical periodic policies algorithm is based on the characteristics of a homo egualis society in which the players also care about the proportional distribution of the pay-off in relation to the pay-off of the other players. This feature is built into a hierarchy of learning automata which is suited for playing sequential decision problems.