A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning

TL;DR

This paper introduces a function approximation approach for high-dimensional inverse reinforcement learning that efficiently estimates policies in complex state spaces, with applications in clinical skill evaluation.

Contribution

It proposes a novel function approximation method ensuring Bellman optimality in high-dimensional settings, enabling efficient inverse reinforcement learning in continuous and large state spaces.

Findings

Accurately estimates policies in simulated environments.

Effectively evaluates clinical practitioners' proficiency.

Maintains linear time complexity relative to action set size.

Abstract

This works handles the inverse reinforcement learning problem in high-dimensional state spaces, which relies on an efficient solution of model-based high-dimensional reinforcement learning problems. To solve the computationally expensive reinforcement learning problems, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function based on the observed human actions for inverse reinforcement learning problems. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle high-dimensional even continuous state spaces efficiently. We test the proposed method in a simulated environment to show its accuracy, and three clinical tasks to show how it can be used to evaluate a doctor's proficiency.