La publicación original está disponible en www.springerlink.com.This paper extends a navigation system implemented as a multi-agent system (MAS). The arbitration mechanism controlling the interactions between the agents was based on manually-tuned bidding functions. A difficulty with hand-tuning is that it is hard to handle situations involving complex tradeoffs. In this paper we explore the suitability of reinforcement learning for automatically tuning agents within a MAS to optimize a complex tradeoff, namely the camera use.Fullbright Joint Research Project and Plan Nacional Project DPI 2000-1352-C02-02. Dídac Busquets holds the CIRIT doctoral scholarship 2000FI-00191.Peer reviewed