A non-asymptotic analysis of adaptive TD(λ) learning in wireless sensor networks

Abstract

Wireless sensor network has been widely used in different fields, such as structural health monitoring and artificial intelligence technology. The routing planning, an important part of wireless sensor network, can be formalized as an optimization problem needing to be solved. In this article, a reinforcement learning algorithm is proposed to solve the problem of optimal routing in wireless sensor networks, namely, adaptive TD( λ ) learning algorithm referred to as ADTD( λ ) under Markovian noise, which is more practical than i.i.d. (identically and independently distributed) noise in reinforcement learning. Moreover, we also present non-asymptotic analysis of ADTD( λ ) with both constant and diminishing step-sizes. Specifically, when the step-size is constant, the convergence rate of O ( 1 / T ) is achieved, where T is the number of iterations; when the step-size is diminishing, the convergence rate of O ~ ( 1 / T ) is also obtained. In addition, the performance of the algorithm is verified by simulation.