# A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms

arXiv: https://arxiv.org/abs/2502.00352 · HTML: https://mlsanity.com/paper/2502.00352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 13 of 13)

- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning](https://mlsanity.com/paper/2010.03950/) (2020)
- [Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic](https://mlsanity.com/paper/2105.05701/) (2021)
- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning](https://mlsanity.com/paper/2408.03084/) (2024)
- [Reward Centering](https://mlsanity.com/paper/2405.09999/) (2024)
- [Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priors](https://mlsanity.com/paper/2409.05712/) (2024)
- [Controlling an Autonomous Vehicle with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.12153/) (2019)
- [A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Network](https://mlsanity.com/paper/2010.05437/) (2020)
- [Differential Temporal Difference Learning](https://mlsanity.com/paper/1812.11137/) (2018)
- [Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon](https://mlsanity.com/paper/2008.11852/) (2020)
- [A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles](https://mlsanity.com/paper/2409.13783/) (2024)

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

- [Shared Backbone PPO for Multi-UAV Communication Coverage with Connection Preservation](https://mlsanity.com/paper/2605.17999/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) shows great potential for optimizing multi-vehicle cooperative driving strategies through the state-action-reward feedback loop, but it still faces challenges such as low sample efficiency. This paper proposes a differentiated reward method based on steady-state transition systems, which incorporates state transition gradient information into the reward design by analyzing traffic flow characteristics, aiming to optimize action selection and policy learning in multi-vehicle cooperative decision-making. The performance of the proposed method is validated in RL algorithms such as MAPPO, MADQN, and QMIX under varying autonomous vehicle penetration. The results show that the differentiated reward method significantly accelerates training convergence and outperforms centering reward and others in terms of traffic efficiency, safety, and action rationality. Additionally, the method demonstrates strong scalability and environmental adaptability, providing a novel approach for multi-agent cooperative decision-making in complex traffic scenarios.