Yanwen Ba

Learning from success: efficient selective learning methods for multi-agent sparse-reward tasks

This paper proposes effective multi-agent selective learning methods to boost sample-efficient training by learning from successful experiences, and adopts a retrogression-based selection method to identify successful agent trajectories from the team rewards.