Combining Parametric and Nonparametric Models for Off-Policy Evaluation

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.

Cited by in corpus (4)

• Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
• Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
• Coarse-Grained Smoothness for RL in Metric Spaces