Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …

Communication is an effective mechanism for coordinating the behavior of multiple agents.
In the field of multi-agent reinforcement learning, agents can improve the overall learning …

Sharing parameters in multi-agent deep reinforcement learning has played an essential role
in allowing algorithms to scale to a large number of agents. Parameter sharing between …

Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …

Multi-agent reinforcement learning (MARL) has witnessed significant progress with the
development of value function factorization methods. It allows optimizing a joint action-value …

Effective communication can improve coordination in cooperative multi-agent reinforcement
learning (MARL). One popular communication scheme is exchanging agents' local …

Many advances in cooperative multi-agent reinforcement learning (MARL) are based on two
common design principles: value decomposition and parameter sharing. A typical MARL …

The necessity for cooperation among intelligent machines has popularised cooperative multi-
agent reinforcement learning (MARL) in AI research. However, many research endeavours …