Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

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 …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

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 …

This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Abstract Partially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm
that can generate approximate policies for large Partially Observable Markov Decision …

A multi-agent partially observable Markov decision process (MPOMDP) is a modeling
paradigm used for high-level planning of heterogeneous autonomous agents subject to …

The POMDP is a powerful framework for reasoning under outcome and information
uncertainty, but constructing an accurate POMDP model is difficult. Bayes-Adaptive Partially …

Intelligent traffic signal control helps to reduce traffic congestion and thus has been studied
for a few decades. Multi-intersection cooperative traffic signal control (CTSC), which is more …