Tactical wireless sensor networks (T-WSNs) are used in critical data-gathering military
operations, such as battlefield surveillance, combat monitoring, and intrusion detection …

Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an
essential approach for extending the lifetime of wireless sensor networks (WSNs), which …

In this paper, we present PRISMA: Packet Routing Simulator for Multi-Agent Reinforcement
Learning. To the best of our knowledge, this is the first tool specifically conceived to develop …

In this study, we examine the problem of downlink wireless routing in integrated access
backhaul (IAB) networks involving fiber-connected base stations, wireless base stations, and …

Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature,
and its powerful results. In this paper, we study a number of reinforcement learning …

Mobile wireless networks present several challenges for any learning system, due to
uncertain and variable device movement, a decentralized network architecture, and …

We propose a learning algorithm for local routing policies that needs only a few data
samples obtained from a single graph while generalizing to all random graphs in a standard …

In recent years, several works have studied Multi-Agent Deep Reinforcement Learning for
the Distributed Packet Routing problem, with promising results in various scenarios where …

Wireless meshes offer a resilient and cost-effective framework where multi-hop
communication occurs among mesh clients, routers, and gateways. In this framework …

One challenging problem in networking is the Distributed Packet Routing (DPR), where the
nodes have to define a routing interface for an incoming packet. The traditional routing …