The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep reinforcement learning and multi-agent reinforcement learning. Research problems include scalable learning of coordinated agent policies and inter-agent communication; reasoning about the behaviours, goals, and composition of other agents from limited observations; and sample-efficient learning based on intrinsic motivation, curriculum learning, causal inference, and representation learning. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.

翻译：为实现这一目标,自治代理研究小组开发了用于自主系统控制的新机器学习算法,特别侧重于深度强化学习和多剂强化学习;研究问题包括:可扩展地学习协调一致的代理政策和代理人之间的交流;从有限的观察中推理其他代理人的行为、目标和组成;基于内在动机、课程学习、因果推断和代表性学习的抽样高效学习;该文章广泛概述了该团体正在进行的研究组合,并讨论了今后方向的公开问题。