# Path Learning with Trajectory Advantage Regression

arXiv: https://arxiv.org/abs/2506.19375 · HTML: https://mlsanity.com/paper/2506.19375/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 2 of 2)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey](https://mlsanity.com/paper/2404.00340/) (2024)

## Built on by (most cited first, 1 of 1)

- [Cross-Process Defect Attribution using Potential Loss Analysis](https://mlsanity.com/paper/2508.00895/) (2025)

## 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.