Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics

1 code implementation • 16 Jul 2019 • Rituraj Kaushik, Pierre Desreumaux, Jean-Baptiste Mouret

Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a planning or learning algorithm chooses the most appropriate policies according to the current situation (e. g., a damaged robot, a new object, etc.).

Meta-Learning RTE