Deep Reinforcement Learning for Multi-Agent Interaction

• URL: http://arxiv.org/abs/2208.01769v1
• Date: Tue, 2 Aug 2022 21:55:56 GMT
• Title: Deep Reinforcement Learning for Multi-Agent Interaction
• Authors: Ibrahim H. Ahmed and Cillian Brewitt and Ignacio Carlucho and Filippos Christianos and Mhairi Dunion and Elliot Fosong and Samuel Garcin and Shangmin Guo and Balint Gyevnar and Trevor McInroe and Georgios Papoudakis and Arrasy Rahman and Lukas Sch\"afer and Massimiliano Tamborski and Giuseppe Vecchio and Cheng Wang and Stefano V. Albrecht
• Abstract summary: The Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.
• Score: 14.532965827043254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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