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Revisiting the Cross Entropy Method with Applications in Stochastic Global Optimization and Reinforcement Learning

Joseph, Ajin George and Bhatnagar, Shalabh (2016) Revisiting the Cross Entropy Method with Applications in Stochastic Global Optimization and Reinforcement Learning. In: 22nd European Conference on Artificial Intelligence (ECAI), AUG 29-SEP 02, 2016, Hague, NETHERLANDS, pp. 1026-1034.

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Official URL: http://dx.doi.org/10.3233/978-1-61499-672-9-1026

Abstract

In this paper, we provide a new algorithm for the problem of stochastic global optimization where only noisy versions of the objective function are available. The algorithm is inspired by the well known cross entropy (CE) method. The algorithm takes the shape of a multi-timescale stochastic approximation algorithm, where we reuse the previous samples based on discounted averaging, and hence it saves the overall computational and storage cost. We provide proof of the stability and the global optimization property of our algorithm. The algorithm shows good performance on the noisy versions of global optimization benchmarks and outperforms a state-of-the-art algorithm for non-linear function approximation in reinforcement learning.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IOS PRESS, NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 03 Dec 2016 09:45
Last Modified: 03 Dec 2016 09:45
URI: http://eprints.iisc.ac.in/id/eprint/55368

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