#ECAI2023 outstanding paper: Interview with Xuan Liu – multi-agent sparse reward tasks

Exploiting high-value experience to provide guidance. Xuan Liu, and colleagues Xinning Chen, Yanwen Ba, Shigeng Zhang, Bo Ding, Kenli Li, won an outstanding paper award at the 26th European Conference on Artificial Intelligence (ECAI 2023). In this interview, Xuan tells us more about their work. Our paper Selective Learning for Sample-Efficient Training in Multi-Agent Sparse Reward Tasks focuses on improving sample efficiency for multi-agent cooperative tasks with sparse reward. Learning effective polices in multi-agent environments with sparse reward is a great challenge for agents as the concurrent learning of multiple agents induces the non-stationarity problem and sharply increases joint state space.