Path Learning with Trajectory Advantage Regression

TL;DR

This paper introduces trajectory advantage regression, a novel offline reinforcement learning method for path learning and attribution that simplifies path optimization to a regression problem.

Contribution

It presents a new approach that reduces path optimization in reinforcement learning to a regression task, enabling efficient offline path learning.

Findings

Effective in offline path learning scenarios

Simplifies path optimization to regression

Potential for improved computational efficiency

Abstract

In this paper, we propose trajectory advantage regression, a method of offline path learning and path attribution based on reinforcement learning. The proposed method can be used to solve path optimization problems while algorithmically only solving a regression problem.