Year: 2019
Type: preprint
Abstract: We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-parametric models of the environment such that the final value estimate has the least expected error. We do so by first estimating the local accuracy of each model and then using a planner to select which model to use at every time step as to minimize the return error estimate along entire trajectories. Across a variety of domains, our mixture-based approach outperforms the individual models alone as well as state-of-the-art importance sampling-based estimators.
Source: arXiv (Cornell University)
Authors: Gottesman, Omer, Liu, Yao, Sussex, Scott, Brunskill, Emma, Doshi-Velez, Finale
Institutions: Harvard University, Stanford University, ETH Zurich
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2946529464