Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

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

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 …

Telecommunication using 5G plays a vital role in our daily lives and the global economy.
However, the energy consumption and carbon emissions of 5G mobile networks are …

Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …

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

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

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

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 …

The availability of challenging benchmarks has played a key role in the recent progress of
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …