Learning To Simulate

Publication Date: 5/6/2019

Event: Seventh International Conference on Learning Representations (ICLR 2019)

Reference: pp. 1-12, 2019

Authors: Nataniel Ruiz, Boston University, NEC Laboratories America, Inc.; Samuel Schulter, NEC Laboratories America, Inc.; Manmohan Chandraker, NEC Laboratories America, Inc.

Abstract: Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.

Publication Link: https://openreview.net/pdf?id=HJgkx2Aqt7

Additional Publication Link: https://arxiv.org/pdf/1810.02513v1.pdf