Deep reinforcement learning has proved to be a fruitful method in various tasks in the field of
artificial intelligence during the last several years. Recent works have focused on deep …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

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 …

Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

In this paper, we introduce a multi-agent multiarmed bandit-based model for ad hoc
teamwork with expensive communication. The goal of the team is to maximize the total …

Multiagent algorithms often aim to accurately predict the behaviors of other agents and find a
best response accordingly. Previous works usually assume an opponent uses a stationary …

Humans, like all animals, both cooperate and compete with each other. Through these
interactions we learn to observe, act, and manipulate to maximise our utility function, and …

Opponent modeling is a longstanding research topic aimed at enhancing decision-making
by modeling information about opponents in multi-agent environments. However, existing …

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 …