Path Learning with Trajectory Advantage Regression

2025-06-24

AI Summary

This work addresses the path optimization challenge in offline path learning and path attribution. We propose Trajectory Advantage Regression (TAR), a novel method that formulates path optimization as a regression problem over advantage functions within a reinforcement learning framework—bypassing conventional policy iteration and computationally expensive search procedures. TAR directly learns high-quality path policies by regressing trajectory-level advantage values conditioned on state-action sequences, thereby enhancing training stability and decision interpretability. Empirical evaluation on offline datasets demonstrates that TAR consistently outperforms existing baselines in path accuracy, robustness, and attribution consistency. Notably, it is the first approach to achieve efficient path optimization under a purely regression-based paradigm. TAR thus provides both a conceptual advance and a practical tool for offline sequential decision-making.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Offline path learning via reinforcement learning
• Path optimization through regression techniques
• Trajectory advantage for path attribution

Innovation

• Trajectory advantage regression for path learning
• Offline path attribution via reinforcement learning
• Path optimization through regression problem solving