… sensor network (WSN) consists of a large number of sensors … Reinforcement learning (RL)
has been applied in a wide … and rate control, so that the sensors and sink nodes are able to …

… -hoc sensor networks. The … reinforcement learning techniques for sensor networks; In Section
4, we describe in detail our routing scheme based on least squares reinforcement learning; …

… sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum
requirements. We turn to reinforcement learning… aim to extend the network lifetime. Our QL-…

… introduced deep learning in reinforcement learning [9], [10] and proposed Deep-Q-Net (DQN…
acquisition ability of deep learning to enable the reinforcement learning algorithm to extract …

… As sensor networks for health monitoring become more preva… By utilising Reinforcement
Learning (RL) techniques, we provide … It achieves this by utilising other sensors available in the …

… in the entire network. In order to … network, we study our resource allocation policies to
manage both power and time for throughput maximization. We use deep reinforcement learning (…

… advocate the use of reinforcement learning for task adaptation and scheduling in wireless
sensor networks. We have presented distributed independent reinforcement learning (DIRL) …

… In this paper, nodes actively infer the state of other nodes, using a reinforcement learning …
wireless sensor networks, reinforcement learning and high-performance machine learning …

… wireless sensor networks is to enable each node to harvest energy in its environment. To …
A novel energy management algorithm based on reinforcement learning (RLMan) is proposed …

… We introduce the reinforcement learning algorithm to dynamically learn this information,
and all of the information will be captured by the reinforcement learning model of the routing …