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Quality-Diversity algorithms, such as MAP-Elites, are a branch ofEvolutionary Computation generating collections of diverse and high-performingsolutions, that have been successfully applied to a variety of domains andparticularly in evolutionary robotics. However, MAP-Elites performs a divergentsearch based on random mutations originating from Genetic Algorithms, and thus,is limited to evolving populations of low-dimensional solutions. PGA-MAP-Elitesovercomes this limitation by integrating a gradient-based variation operatorinspired by Deep Reinforcement Learning which enables the evolution of largeneural networks. Although high-performing in many environments, PGA-MAP-Elitesfails on several tasks where the convergent search of the gradient-basedoperator does not direct mutations towards archive-improving solutions. In thiswork, we present two contributions: (1) we enhance the Policy Gradientvariation operator with a descriptor-conditioned critic that improves thearchive across the entire descriptor space, (2) we exploit the actor-critictraining to learn a descriptor-conditioned policy at no additional cost,distilling the knowledge of the archive into one single versatile policy thatcan execute the entire range of behaviors contained in the archive. Ouralgorithm, DCG-MAP-Elites improves the QD score over PGA-MAP-Elites by 82% onaverage, on a set of challenging locomotion tasks.

MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy