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.

In this article, we are intested in the evolution of specialisation among a single population of heterogeneous robotic agents in a cooperative foraging task. In particular, we want to compare (1) the emergence and (2) fixation of genotypic polymorphism under two different selection methods: elitist and fitness- proportionate. We show that, while the emergence of specialists is easy under an elitist selection, this method cannot maintain heterogeneous behaviours throughout the whole simulation. In comparison a fitness-proportionate algorithm proves to be inefficient in evolving any cooperative strategy but ensures the conservation of heterogeneity when it is present in the population. We then reveal through additional experiments two key factors for the evolution of heterogenous behaviours in our task: (1) protection of genotypic diversity and (2) efficient selection of partners. We finally demonstrate this assertion and, while our main problem remains unsolved, we provide directions on how it could be successfully approached.