There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …

A fundamental challenge in multiagent reinforcement learning is to learn beneficial
behaviors in a shared environment with other simultaneously learning agents. In particular …

Due to the non-stationary environment, learning in multi-agent systems is a challenging
problem. This paper first introduces a new gradient-based learning algorithm, augmenting …

Recent developments in deep reinforcement learning are concerned with creating decision-
making agents which can perform well in various complex domains. A particular approach …

Reinforcement Learning was originally developed for Markov Decision Processes (MDPs). It
allows a single agent to learn a policy that maximizes a possibly delayed reward signal in a …

Difference rewards and potential-based reward shaping can both significantly improve the
joint policy learnt by multiple reinforcement learning agents acting simultaneously in the …

Multi-agent reinforcement learning (MARL) is a widely researched technique for
decentralised control in complex large-scale autonomous systems. Such systems often …

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used
evaluation tasks and criteria, making comparisons between approaches difficult. In this work …

Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …

This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …