Limiting Extrapolation in Linear Approximate Value Iteration

Neural Information Processing Systems (NeurIPS)


We study linear approximate value iteration (LAVI) with a generative model. While linear models may accurately represent the optimal value function using a few parameters, several empirical and theoretical studies show the combination of least-squares projection with the Bellman operator may be expansive, thus leading LAVI to amplify errors over iterations and eventually diverge. We introduce an algorithm that approximates value functions by combining Q-values estimated at a set of anchor states. Our algorithm tries to balance the generalization and compactness of linear methods with the small amplification of errors typical of interpolation methods. We prove that if the features at any state can be represented as a convex combination of features at the anchor points, then errors are propagated linearly over iterations (instead of exponentially) and our method achieves a polynomial sample complexity bound in the horizon and the number of anchor points. These findings are confirmed in preliminary simulations in a number of simple problems where a traditional least-square LAVI method diverges.

Related Publications

All Publications

CVPR - June 18, 2021

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi

CVPR - June 18, 2021

Discovering Relationships between Object Categories via Universal Canonical Maps

Natalia Neverova, Artsiom Sanakoyeu, Patrick Labatut, David Novotny, Andrea Vedaldi

CVPR - June 17, 2021

Connecting What to Say With Where to Look by Modeling Human Attention Traces

Zihang Meng, Licheng Yu, Ning Zhang, Tamara Berg, Babak Damavandi, Vikas Singh, Amy Bearman

DSN - June 21, 2021

Near-Realtime Server Reboot Monitoring and Root Cause Analysis in a Large-Scale System

Fred Lin, Bhargav Bolla, Eric Pinkham, Neil Kodner, Daniel Moore, Amol Desai, Sriram Sankar

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy