Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

As an emerging technique, mobile-edge computing (MEC) introduces a new scheme for
various distributed communication-computing systems, such as industrial Internet of Things …

Cooperative multi-agent reinforcement learning often requires decentralised policies, which
severely limit the agents' ability to coordinate their behaviour. In this paper, we show that …

We investigate how reinforcement learning agents can learn to cooperate. Drawing
inspiration from human societies, in which successful coordination of many individuals is …

Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-
stationary environments and exponentially increasing policy space. It would be even more …

This paper aims to develop a paradigm that models the learning behavior of intelligent
agents (including but not limited to autonomous vehicles, connected and automated …

In most urban areas, traffic congestion is a vexing, complex and growing issue day by day.
Reinforcement learning (RL) enables a single decision maker (or an agent) to learn and …

Framed in positive terms, this report examines how technical AI research might be steered in
a manner that is more attentive to humanity's long-term prospects for survival as a species …