mlsanityData to 14 Oct 2026

Path Learning with Trajectory Advantage Regression

arXiv 2506.19375 ↗First posted 24 Jun 2025cs.LGcited by 1

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 6 references that are in the map. First posted on arXiv 24 Jun 2025.

Builds on

2 of 6 references.

References · 2

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Surveycited by 5

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Cross-Process Defect Attribution using Potential Loss Analysiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?
  2. 2026Trajectory-Level Data Augmentation for Offline Reinforcement Learning
  3. 2024TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents
  4. 2026Shortcut Trajectory Planning for Efficient Offline Reinforcement Learning
  5. 2022Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality
  6. 2023Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting
  7. 2024Trajectory-Oriented Policy Optimization with Sparse Rewards
  8. 2026ABC: Advantage-Based Control Variates for Reinforcement Learning with Verifiable Rewards