Learning to Cooperate in a Socially Optimal Way in Swarm Robotics

Abstract

This paper addresses the problem of learning cooperative strategies in swarm robotics. We are interested in heterogeneous swarms, in which each robot optimizes its individual gain. For some tasks, the problem is that the optimal strategy requires to cooperate and may be counter-selected in favor of a more stable but less efficient selfish strategy. To solve this problem, we introduce a mechanism of partner choice, which conditions of operation are learned. This mechanism proves surprisingly efficient, when the swarm size is large, and the duration of interactions is long. Beyond evolutionary swarm robotics, the results we present are relevant for other distributed on-line learning methods for robotics, and as a possible extension of existing evolutionary biology and social learning models.

Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate with

“…The situation that is modelled here corresponds to many collective tasks observed in nature [3,25,28], where each agent has to balance between looking for partners and cooperating with the current partner, the latter possibly taking significant time. As a matter of fact, it has been shown elsewhere [4,7,9,10,17] that optimal partner choice strategies can be reached only when the cost of cooperation is large (ie. the duration of cooperation is long with regards to looking for cooperative partners).…”