# Strategic bidding in freight transport using deep reinforcement learning

arXiv: https://arxiv.org/abs/2102.09253 · HTML: https://mlsanity.com/paper/2102.09253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [The power of deeper networks for expressing natural functions](https://mlsanity.com/paper/1705.05502/) (2017)
- [Smart Containers With Bidding Capacity: A Policy Gradient Algorithm for Semi-Cooperative Learning](https://mlsanity.com/paper/2005.00565/) (2020)

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

- [Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey](https://mlsanity.com/paper/2508.20315/) (2025)

## Abstract (arXiv)

This paper presents a multi-agent reinforcement learning algorithm to represent strategic bidding behavior in freight transport markets. Using this algorithm, we investigate whether feasible market equilibriums arise without any central control or communication between agents. Studying behavior in such environments may serve as a stepping stone towards self-organizing logistics systems like the Physical Internet. We model an agent-based environment in which a shipper and a carrier actively learn bidding strategies using policy gradient methods, posing bid- and ask prices at the individual container level. Both agents aim to learn the best response given the expected behavior of the opposing agent. A neutral broker allocates jobs based on bid-ask spreads. Our game-theoretical analysis and numerical experiments focus on behavioral insights. To evaluate system performance, we measure adherence to Nash equilibria, fairness of reward division and utilization of transport capacity. We observe good performance both in predictable, deterministic settings (~95% adherence to Nash equilibria) and highly stochastic environments (~85% adherence). Risk-seeking behavior may increase an agent's reward share, as long as the strategies are not overly aggressive. The results suggest a potential for full automation and decentralization of freight transport markets.