Reinforcement social learning of social optimality with influencer agents

摘要

In many multiagent systems (MAS), it is desirable that the agents can coordinate with one another on achieving socially optimal outcomes to increase the system level performance, and the traditional way of attaining this goal is to endow the agents with social rationality [in: Proc. of AAAI Fall Symposium on Socially Intelligent Agents, 1997, pp. 61-63] - agents act as system utility maximizers. However, this is difficult to implement when we are facing open MAS domains such as peer-to-peer network and mobile ad-hoc networks, since we do not have control on all agents' behaviors in such systems and each agent usually behaves individually rationally as an individual utility maximizer only. In this paper, we propose injecting a number of influencer agents [in: Proc. of AAMAS'13, ACM Press, 2013, pp. 447-454, AAMAS (2012)] to manipulate the behaviors of individually rational agents and investigate whether the individually rational agents can eventually be incentivized to coordinate on achieving socially optimal outcomes. We evaluate the effects of influencer agents in two common types of games: prisoner's dilemma games and anti-coordination games. Simulation results show that a small proportion of influencer agents can significantly increase the average percentage of socially optimal outcomes attained in the system and better performance can be achieved compared with that of previous work.