With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …

The interaction of multiple autonomous agents gives rise to highly dynamic and
nondeterministic environments, contributing to the complexity in applications such as …

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 …

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …

Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …

This paper investigates the spectrum sharing problem in vehicular networks based on multi-
agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the …

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 …

We explore deep reinforcement learning methods for multi-agent domains. We begin by
analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is …

This work considers the problem of learning cooperative policies in complex, partially
observable domains without explicit communication. We extend three classes of single …

Many real-world problems, such as network packet routing and urban traffic control, are
naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing …