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.
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.
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.
The symbol is an abstract representation of a process in which two agents converge to an equilibrium point via repeated interaction.