# Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems

arXiv: https://arxiv.org/abs/2112.00424 · HTML: https://mlsanity.com/paper/2112.00424/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03882/) (2019)
- [Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning](https://mlsanity.com/paper/1709.04083/) (2017)
- [Teaching on a Budget in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1905.01357/) (2019)
- [Learning to Teach Reinforcement Learning Agents](https://mlsanity.com/paper/1707.09079/) (2017)
- [Smart City Development with Urban Transfer Learning](https://mlsanity.com/paper/1808.01552/) (2018)
- [Pattern Transfer Learning for Reinforcement Learning in Order Dispatching](https://mlsanity.com/paper/2105.13218/) (2021)

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

- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Causal Knowledge Transfer for Multi-Agent Reinforcement Learning in Dynamic Environments](https://mlsanity.com/paper/2507.13846/) (2025)
- [Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2303.01170/) (2023)
- [Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2501.15495/) (2025)
- [Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentives](https://mlsanity.com/paper/2510.13227/) (2025)

## Abstract (arXiv)

Reinforcement learning (RL) has been used in a range of simulated real-world tasks, e.g., sensor coordination, traffic light control, and on-demand mobility services. However, real world deployments are rare, as RL struggles with dynamic nature of real world environments, requiring time for learning a task and adapting to changes in the environment. Transfer Learning (TL) can help lower these adaptation times. In particular, there is a significant potential of applying TL in multi-agent RL systems, where multiple agents can share knowledge with each other, as well as with new agents that join the system. To obtain the most from inter-agent transfer, transfer roles (i.e., determining which agents act as sources and which as targets), as well as relevant transfer content parameters (e.g., transfer size) should be selected dynamically in each particular situation. As a first step towards fully dynamic transfers, in this paper we investigate the impact of TL transfer parameters with fixed source and target roles. Specifically, we label every agent-environment interaction with agent's epistemic confidence, and we filter the shared examples using varying threshold levels and sample sizes. We investigate impact of these parameters in two scenarios, a standard predator-prey RL benchmark and a simulation of a ride-sharing system with 200 vehicle agents and 10,000 ride-requests.