Professor Garratt wins Best Empirical Paper Award at Neural Information Processing Systems Workshop

Professor Garratt won the Best Empirical Paper Award at the 2020 Neural Information Processing Systems (NeurIPS) Workshop. The workshop, which is held annually, explores the adoption of machine learning for Economic policy.

Garratt's paper, "Estimating Policy Functions in Payment Systems using Reinforcement Learning," discusses the use of deep reinforcement learning for estimating participant behavior in payment systems. The paper is written with co-authors from the Bank of Canada and Google Brain.