BAYESIAN SEQUENTIAL OPTIMAL EXPERIMENTAL DESIGN FOR INVERSE PROBLEMS USING DEEP REINFORCEMENT LEARNING

We perform a comprehensive study on Bayesian sequential optimal experimental design techniquesapplied to inverse problems. We transform the Bayesian sequential optimal experimental design problem into a reinforcement learning problem to gauge the power of recent deep reinforcement learning algorithms compared to other baseline algorithms. Using KL-divergence as a measure of information gain, we construct objectives to maximize information gain for batch design, greedy design, black-box Bayesian optimization, multi-armed bandit optimization, dynamic programming, approximate dynamic programming, and reinforcement learning. This work showcases novel comparisons between the aforementioned methods and a new application of off-the-shelf reinforcement learning algorithms to Bayesian sequential optimal experimental design for inverse problems in differential equation models.

Author dc:creator

• Anderson, Loren

• Bayesian Experimental Design
• Deep Reinforcement Learning
• Information Gain
• Inverse Problems
• Optimal Experimental Design
• Sequential Experimental Design

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/11299/241402

OAI identifier oai:identifier

oai:conservancy.umn.edu:11299/241402