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In this paper, we study the outage minimization problem in adecode-and-forward cooperative network with relay uncertainty. To reduce theoutage probability and improve the quality of service, existing researchesusually rely on the assumption of both exact instantaneous channel stateinformation (CSI) and environmental uncertainty. However, it is difficult toobtain perfect instantaneous CSI immediately under practical situations wherechannel states change rapidly, and the uncertainty in communicationenvironments may not be observed, which makes traditional methods notapplicable. Therefore, we turn to reinforcement learning (RL) methods forsolutions, which do not need any prior knowledge of underlying channel orassumptions of environmental uncertainty. RL method is to learn from theinteraction with communication environment, optimize its action policy, andthen propose relay selection and power allocation schemes. We first analyse therobustness of RL action policy by giving the lower bound of the worst-caseperformance, when RL methods are applied to communication scenarios withenvironment uncertainty. Then, we propose a robust algorithm for outageprobability minimization based on RL. Simulation results reveal that comparedwith traditional RL methods, our approach has better generalization ability andcan improve the worst-case performance by about 6% when evaluated in unseenenvironments.

Reinforcement Learning Based Robust Policy Design for Relay and Power Optimization in DF Relaying Networks