… multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement
learning … into account in their extension to multi-agent scenarios. The analyzed algorithms …

… reinforcement learning (RL), which has registered tremendous success in solving various
sequential decision-making problems in machine learning… the realm of multi-agent RL (MARL), …

… case studies of multiagent reinforcement learning involving such cooperation and draws
some related conclusions that are not limited to multi-agent reinforcement learning. The main …

… and challenges of multi-agent reinforcement learning are … a multi-agent learning goal; this
chapter reviews the learning goals … where multi-agent reinforcement learning techniques have …

… advances in multi-agent reinforcement learning (MARL) techniques. MARL corresponds to
the learning problem in a multi-agent system in which multiple agents learn simultaneously. It …

… We have reviewed the challenges of multi-agent reinforcement learning, the methods to
address them, and we have provided specific conclusions and open issues for each class of …

… Our approach is programmatic: first, we propose a set of multi-agent benchmark tasks …
learning algorithms for these tasks; finally, we analyse how these algorithms learn, or fail to learn, …

… about multi-agent environments. In particular, the paper describes a reinforcement learning
… in the update step of a standard Q-learning algorithm is replaced by a "minimax" operator …

… other learners or by competing with them. This chapter focuses on the application reinforcement
learning techniques in multi-agent systems. We describe a basic learning framework …

… Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement
learning (… architecture which leverages the multi-agent advantage decomposition theorem to …