# Generalizing Cooperative Eco-driving via Multi-residual Task Learning

arXiv: https://arxiv.org/abs/2403.04232 · HTML: https://mlsanity.com/paper/2403.04232/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Residual Policy Learning](https://mlsanity.com/paper/1812.06298/) (2018)
- [Learning Continuous Control Policies by Stochastic Value Gradients](https://mlsanity.com/paper/1510.09142/) (2015)
- [Learning-based Model Predictive Control for Safe Exploration](https://mlsanity.com/paper/1803.08287/) (2018)
- [Gaussian Processes for Data-Efficient Learning in Robotics and Control](https://mlsanity.com/paper/1502.02860/) (2015)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Contextualize Me -- The Case for Context in Reinforcement Learning](https://mlsanity.com/paper/2202.04500/) (2022)
- [Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing](https://mlsanity.com/paper/1808.03246/) (2018)
- [Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty](https://mlsanity.com/paper/2001.09336/) (2020)
- [The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning](https://mlsanity.com/paper/2210.08607/) (2022)
- [Learning Eco-Driving Strategies at Signalized Intersections](https://mlsanity.com/paper/2204.12561/) (2022)

## Built on by (most cited first, 5 of 5)

- [Model-Based Transfer Learning for Contextual Reinforcement Learning](https://mlsanity.com/paper/2408.04498/) (2024)
- [IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning](https://mlsanity.com/paper/2410.15221/) (2024)
- [Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale](https://mlsanity.com/paper/2408.05609/) (2024)
- [Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems](https://mlsanity.com/paper/2507.09836/) (2025)
- [REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement](https://mlsanity.com/paper/2609.21108/) (2026)

## Abstract (arXiv)

Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends with a multitude of diverse traffic scenarios that are challenging for these planning algorithms. Model-free Deep Reinforcement Learning (DRL) presents a promising avenue in this direction, but learning DRL control policies that generalize to multiple traffic scenarios is still a challenge. To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning. We employ MRTL for fleet-level emission reduction in mixed traffic using autonomous vehicles as a means of system control. By analyzing the performance of MRTL across nearly 600 signalized intersections and 1200 traffic scenarios, we demonstrate that it emerges as a promising approach to synergize the strengths of DRL and conventional methods in generalizable control.