Machine learning is increasingly used to automate networking tasks, in a paradigm known
as zero touch network and service management (ZSM). In particular, deep reinforcement …

Deep reinforcement learning has made significant development in recent years, and it is
currently applied not only in simulators and games but also in embedded systems. However …

We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …

Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …

The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy
is learned from a static dataset, is compelling as progress enables RL methods to take …

Mobile networks are increasingly expected to support use cases with diverse performance
expectations at a very high level of reliability. These expectations imply the need for …

Reinforcement learning (RL) has emerged as an effective approach for building an
intelligent system, which involves multiple self-operated agents to collectively accomplish a …

While deep reinforcement learning (RL) is becoming an integral part of good decision-
making in data science, it is still plagued with sample inefficiency. This can be challenging …

In different wireless network scenarios, multiple network entities need to cooperate in order
to achieve a common task with minimum delay and energy consumption. Future wireless …

Performance optimization of wireless networks is typically complicated because of high
computational complexity and dynamic channel conditions. Considering a specific case, the …