# A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network

arXiv: https://arxiv.org/abs/1903.00714 · HTML: https://mlsanity.com/paper/1903.00714/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 22 of 22)

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- [Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey](https://mlsanity.com/paper/2508.20315/) (2025)
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- [Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs](https://mlsanity.com/paper/2207.12509/) (2022)
- [Towards More Efficient Shared Autonomous Mobility: A Learning-Based Fleet Repositioning Approach](https://mlsanity.com/paper/2210.08659/) (2022)
- [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024)
- [Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games](https://mlsanity.com/paper/2503.22779/) (2025)
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## Abstract (arXiv)

Resource balancing within complex transportation networks is one of the most important problems in real logistics domain. Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting. However, the high complexity of transportation routes, severe uncertainty of future demand and supply, together with non-convex business constraints make it extremely challenging in the traditional resource management field. In this paper, we propose a novel sophisticated multi-agent reinforcement learning approach to address these challenges. In particular, inspired by the externalities especially the interactions among resource agents, we introduce an innovative cooperative mechanism for state and reward design resulting in more effective and efficient transportation. Extensive experiments on a simulated ocean transportation service demonstrate that our new approach can stimulate cooperation among agents and lead to much better performance. Compared with traditional solutions based on combinatorial optimization, our approach can give rise to a significant improvement in terms of both performance and stability.