# Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing

arXiv: https://arxiv.org/abs/2003.03433 · HTML: https://mlsanity.com/paper/2003.03433/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Stronger-MAS: Multi-Agent Reinforcement Learning for Collaborative LLMs](https://mlsanity.com/paper/2510.11062/) (2025)
- [Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2305.10865/) (2023)
- [Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies](https://mlsanity.com/paper/2410.10212/) (2024)
- [Dynamic neighbourhood optimisation for task allocation using multi-agent](https://mlsanity.com/paper/2102.08307/) (2021)
- [Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2601.21523/) (2026)

## Abstract (arXiv)

In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior. Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents. In this paper, we study reward design problem in cooperative MARL based on packet routing environments. Firstly, we show that the above two reward signals are prone to produce suboptimal policies. Then, inspired by some observations and considerations, we design some mixed reward signals, which are off-the-shelf to learn better policies. Finally, we turn the mixed reward signals into the adaptive counterparts, which achieve best results in our experiments. Other reward signals are also discussed in this paper. As reward design is a very fundamental problem in RL and especially in MARL, we hope that MARL researchers can rethink the rewards used in their systems.