70 research outputs found
Deep Reinforcement Learning for Multi-Agent Interaction
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.Comment: Published in AI Communications Special Issue on Multi-Agent Systems
Research in the U
Chats in Chinese. A translation of the Tan lun xin bian,
Titlte also in Chinese characters on t.p.Mode of access: Internet
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