A few good agents: multi-agent social learning

By Jean Oh, Stephen F. Smith. 2008

24 citations recorded by the source corpus.

In this paper, we investigate multi-agent learning (MAL) in a multi-agent resource selection problem (MARS) in which a large group of agents are competing for common resources. Since agents in such a setting are self-interested, MAL in MARS domains typically focuses on the convergence to a set of non-cooperative equilibria. As seen in the example of prisoner's dilemma, however, selfish equilibria are not necessarily optimal with respect to the natural objective function of a target problem, e.g., resource utilization in the case of MARS. Conversely, a centrally administered optimization of physically distributed agents is infeasible in many real-life applications such as transportation traffic problems. In order to explore the possibility for a middle ground solution, we analyze two types of costs for evaluating MAL algorithms in this context. The quality loss of a selfish algorithm can be quantitatively measured by the price of anarchy, i.e., the ratio of the objective function value of a selfish solution to that of an optimal solution. Analogously, we introduce the price of monarchy of a learning algorithm to quantify the practical cost of coordination in terms of communication cost. We then introduce a multi-agent social learning approach named A Few Good Agents (AFGA) that motivates self-interested agents to cooperate with one another to reduce the price of anarchy, while bounding the price of monarchy at the same time. A preliminary set of experiments on the El Farol bar problem, a simple example of MARS, show promising results.

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Citations in the corpus

Multi-Agent Common Knowledge Reinforcement Learning (2018, background): “Oh & Smith (2008) employ a hierarchical learning algorithm for cooperative control tasks where the outer layer decides whether an agent should coordinate or act independently, and the inner layer then chooses the agent’s action accordingly.”