In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in
a shared environment to achieve a common goal. We propose the use of reward machines …

Information sharing is key in building team cognition and enables coordination and
cooperation. High-performing human teams also benefit from acting strategically with …

ABSTRACT A major challenge for traditional approaches to multiagent learning is to train
teams that easily scale to include additional agents. The problem is that such approaches …

Centralised training with decentralised execution is an important setting for cooperative
deep multi-agent reinforcement learning due to communication constraints during execution …

High-performing teams learn effective communication strategies to judiciously share
information and reduce the cost of communication overhead. Within multi-agent …

The concept of creating autonomous agents capable of exhibiting ad hoc teamwork was
recently introduced as a challenge to the AI, and specifically to the multiagent systems …

Many real-world tasks involve multiple agents with partial observability and limited
communication. Learning is challenging in these settings due to local viewpoints of agents …

Inspired by recent advances in single agent reinforcement learning, this paper extends the
single-agent asynchronous advantage actor-critic (A3C) algorithm to enable multiple agents …

Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to
enable humans to gain insight into the decision-making of machine learning models …

In many real-world multi-agent systems, the sparsity of team rewards often makes it difficult
for an algorithm to successfully learn a cooperative team policy. At present, the common way …