In this paper, we investigate how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system. Today’s decision making in most wireless systems include cognitive radio systems in development, depends purely on instantaneous measurement. Two system architectures have been investigated in this paper. A point-to-point a...

TAO JIANG, Ph.D. THESIS, COMMUNICATIONS RESEARCH GROUP, UNIVERSITY OF YORK 2011 Abstract This thesis investigates how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system. Decision making in most wireless systems today, including most cognitive radio systems in development, depends purely on instantaneous meas...

This paper shows that the distributed representation found in Learning Vector Quantization (LVQ) enables reinforcement learning methods to cope with a large decision search space, defined in terms of equivalence classes of input patterns like those found in the game of Go. In particular, this paper describes S[arsa]LVQ, a novel reinforcement learning algorithm and shows its feasibility for patt...

Distributed-air-jet MEMS-based systems have been proposed to manipulate small parts with high velocities and without any friction problems. The control of such distributed systems is very challenging and usual approaches for contact arrayed system don’t produce satisfactory results. In this paper, we investigate reinforcement learning control approaches in order to position and convey an object...

| We present a neural eld approach to distributed Q-learning in continuous state and action spaces that is based on action coding and selection in dynamic neural elds. It is, to the best of our knowledge, one of the rst attempts that combines the advantages of a topological action coding with a distributed action-value learning in one neural architecture. This combination, supplemented by a neu...

In this paper we focus on the application of reinforcement learning to obstacle avoidance in dynamic Environments in wireless sensor networks. A distributed algorithm based on reinforcement learning is developed for sensor networks to guide mobile robot through the dynamic obstacles. The sensor network models the danger of the area under coverage as obstacles, and has the property of adoption o...

This paper reports on experiments involving a hexapod robot. Motivated by neurobiological evidence that control in real hexapod insects is distributed leg-wise, we investigated two approaches to learning distributed controllers: genetic algorithms and reinforcement learning. In the case of reinforcement learning, a new learning algorithm was developed to encourage cooperation between legs. Resu...

in this paper an adaptive pid controller for wind energy conversion systems (wecs) has been developed. theadaptation technique applied to this controller is based on reinforcement learning (rl) theory. nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides 2 ge...