Nils Morozs

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—This paper presents the concept of the Win-or-Learn-Fast (WoLF) variable learning rate for distributed Q-learning based dynamic spectrum management algorithms. It demonstrates the importance of choosing the learning rate correctly by simulating a large scale stadium temporary event network. The results show that using the WoLF variable learning rate(More)
—In this paper we propose an algorithm for dynamic spectrum access (DSA) in LTE cellular systems-distributed ICIC accelerated Q-learning (DIAQ). It combines distributed reinforcement learning (RL) and standardized inter-cell interference coordination (ICIC) signalling in the LTE downlink, using the framework of heuristically accelerated RL (HARL).(More)
—In this paper we propose a novel Bayesian network based model for analysing convergence properties of reinforcement learning (RL) based dynamic spectrum access (DSA) algorithms. It uses a minimum complexity DSA problem for prob-abilistic analysis of the joint policy transitions of RL algorithms. A Monte Carlo simulation of a distributed Q-learning DSA(More)