Towards Safe Policy Improvement for Non-Stationary MDPs

Neural Information Processing Systems (NeurIPS 2020)

Published December 6, 2020

Yash Chandak, Scott Jordan, Georgios Theocharous, Martha White, Philip S. Thomas


Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that are safe for deployment, they assume that the underlying problem is stationary. However, many real-world problems of interest exhibit non-stationarity, and when stakes are high, the cost associated with a false stationarity assumption may be unacceptable. We take the first steps towards ensuring safety, with high confidence, for smoothly-varying non-stationary decision problems. Our proposed method extends a type of safe algorithm, called a Seldonian algorithm, through a synthesis of model-free reinforcement learning with time-series analysis. Safety is ensured using sequential hypothesis testing of a policy's forecasted performance, and confidence intervals are obtained using wild bootstrap.

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Research Area:  AI & Machine Learning