… MAS) on a wireless sensor network comprising sensor-actuator … In this paper; several
distributed reinforcement learning (DRL) … tasks in resourceconstrained wireless sensor networks. …

… a distributed Reinforcement Learning (RL) framework for synthesizing wireless network
protocols in IoT and Wireless Sensor Networks … using distributed Reinforcement Learning as a …

… reinforcement learning in wireless sensor networks. This paper presents an implementation
of one of the reinforcement learning algorithms ie Nash Q-learning … of reinforcement learning …

… , Multiagent systems, Distributed reinforcement learning … in order to maximize the sensing
coverage of an area while … the effectiveness of distributed reinforcement learning (DRL) …

Distributed reinforcement learning algorithms for collaborative multi-agent Markov decision
processes (MDPs) are presented and analyzed. The networked setup consists of a collection …

… the existing sensor network coordination algorithms and pitted them against each other to
investigate how well they perform concerning two major issues in the wireless sensor network; …

… Reinforcement learning techniques were used to solve a … reinforcement learning (Q-learning)
to expand distributed approaches for Coverage Holes problems in mobile sensor networks. …

… We show that our two-tier reinforcement learning scheme is significantly more efficient than
traditional approaches to resource management while fulfilling the application requirements. …

… In this paper, we advocate the use of reinforcement learning for task adaptation and … in
wireless sensor networks. We have presented distributed independent reinforcement learning (…

In this paper, a distributed reinforcement-learning (RL) algorithm is proposed for power …
multi-hop wireless-sensor-networks (WSNs). The WSN consists of M EH sensor nodes aiming to …