… In this paper a reinforcement learning-based sleep scheduling for coverage (RLSSC)
algorithm is proposed for solarpowered wireless sensor networks. We adopt a two-stage sleep …

… to exploit the dynamic nature of network traffic in reducing energy … reinforcement learning
algorithm for the base station such that it can continuously adapt to the ever-changing network …

… learning algorithm that uses deep 5 network to approximate the 5 value action function. Deep
reinforcement learning … enabled opportunistic IA wireless networks. Simulation results are …

… We adopt the reinforcement learning (RL) framework to learn the optimal controller for each
UE, which makes HO decisions. We incorporate the situation and exploration information of …

… adopting offline reinforcement learning [2] for wireless network … suitable for wireless RRM,
because in practice wireless operators … RL to the domain of wireless network optimization. This …

… By utilising Reinforcement Learning (RL) techniques, we provide an adaptive framework,
which continuously performs weak training in an energy-aware system. We motivate this using …

… The control loop is built on top of a software-defined wireless network controller (in our case,
the Ethanol [10] communication layer). Both programs run in the same host, and both run as …

… can be formulated as wireless version of the NP-hard Virtual Network Embedding (VNE) …
We propose a Reinforcement Learning (RL) framework, which relies on QLearning and uses …

… As the optimal joint scheduling and routing problem for multi-hop wireless networks is NP-…
REinforcement learning method for joint routing and sCheduling in time-ConstrainEd networks …

… by reformulating the Markov problem as a Reinforcement Learning (RL) [12] one and
proposed a power controller for a generic contention-based wireless network. Their RL solution is …