While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL),
covering MARL's models, solution concepts, algorithmic ideas, technical challenges, and …

Communication requires having a common language, a lingua franca, between agents. This
language could emerge via a consensus process, but it may require many generations of …

Ad hoc teamwork is the research problem of designing agents that can collaborate with new
teammates without prior coordination. This survey makes a two-fold contribution: First, it …

Modelling the behaviours of other agents is essential for understanding how agents interact
and making effective decisions. Existing methods for agent modelling commonly assume …

Ad hoc teamwork is the well-established research problem of designing agents that can
collaborate with new teammates without prior coordination. This survey makes a two-fold …

Robustly cooperating with unseen agents and human partners presents significant
challenges due to the diverse cooperative conventions these partners may adopt. Existing …

Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior
coordination. Many works propose to abstract teammate instances into high-level …

State of the art methods for ad hoc teamwork, ie, for collaboration without prior coordination,
often use a long history of prior observations to model the behavior of other agents (or agent …

Decentralized multi-agent cooperative learning is a practical task due to the partially
observed setting both in training and execution. Every agent learns to cooperate without …