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

The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

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 …

We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in
the centralized training with decentralized execution (CTDE) regime popularized recently …

In the last few years, deep multi-agent reinforcement learning (RL) has become a highly
active area of research. A particularly challenging class of problems in this area is partially …

In many real-world settings, a team of agents must coordinate its behaviour while acting in a
decentralised fashion. At the same time, it is often possible to train the agents in a …

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

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 …

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

Communication could potentially be an effective way for multi-agent cooperation. However,
information sharing among all agents or in predefined communication architectures that …