This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

In multiagent domains, coping with non-stationary agents that change behaviors from time to
time is a challenging problem, where an agent is usually required to be able to quickly …

Learning in automated negotiations, while successful for many tasks in recent years, is still
hard when coping with different types of opponents with unknown strategies. It is critically …

Despite the significant demand for assistive technology among vulnerable groups (eg the
elderly children and the disabled) in daily tasks research into advanced AI-driven assistive …

While advances in multi-agent learning have enabled the training of increasingly complex
agents, most existing techniques produce a final policy that is not designed to adapt to a …

The Iterated Prisoner's Dilemma has guided research on social dilemmas for decades.
However, it distinguishes between only two atomic actions: cooperate and defect. In real …

Interactions in multiagent systems are generally more complicated than single agent ones.
Game theory provides solutions on how to act in multiagent scenarios; however, it assumes …

Deep reinforcement learning is an agent modeling method with both deep learning feature
extraction ability and reinforcement learning sequence decision-making ability, which can …

Interactive partially observable Markov decision processes (I-POMDPs) provide a principled
framework for planning and acting in a partially observable, stochastic and multi-agent …