This blog post looks at some common inaccuracies in recent multi-agent reinforcement learning research and provides recommendations.
An overview of a range of multi-agent learning environments with some of their properties, main challenges, and practical suggestions.
New online talk series on multi-agent systems research hosted by the Multi-Agent Systems special interest group at the Alan Turing Institute.
We integrate goal recognition and Monte Carlo Tree Search to create an interpretable planning and prediction system for autonomous driving.
In this post, we present two new multi-agent RL environments and a new algorithm which achieves state-of-the-art performance in these environments.
A list of multi-agent reinforcement learning surveys in reverse chronological order.
A list of single-agent reinforcement learning surveys in reverse chronological order.
New technical contributions in autonomous agents modelling other agents, and research perspectives about current challenges and future directions.
The UK Multi-Agent Systems Symposium gathered world-leading UK-based research labs from academia and industry to discuss the current landscape and future of Multi-Agent Systems research.
The symbol is an abstract representation of a process in which two agents converge to an equilibrium point via repeated interaction.
Journal special issue and workshop series presenting new technical contributions in multiagent interaction without prior coordination.
A number of prominent scientists, inventors, and entrepreneurs openly voiced their concerns regarding the potential dangers and risks of artificial intelligence.
Games allow us to study different aspects of interaction systematically and in isolation by providing well-defined environments for interactive decision making.