Deep Reinforcement Learning for Multi-Agent Interaction
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 41 of its 62 references that are in the map. First posted on arXiv 2 Aug 2022.
Builds on
41 of 62 references.
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29 more
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.
3 papers.