Multiagent reinforcement learning has shown its potential for tackling real world problems, like traffic. We consider the toll-based route choice problem, where self-interested agents repeatedly commute attempting to minimise their travel costs. In this paper, we introduce Generalised Toll-based Q-learning (GTQ-learning), a multiagent reinforcement learning algorithm capable of realigning agents' heterogeneous preferences over travel time and monetary expenses to obtain a system-efficient equilibrium. GTQ-learning also includes a mechanism to enforce agents to truthfully report their preferences. Our theoretical analysis and empirical results show that GTQ-learning minimises congestion on realistic road networks.
De Oliveira Ramos, G, Radulescu, R, Nowe, A & Tavares, A 2020, Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences. in B An, A El Fallah Seghrouchni & G Sukthankar (eds), Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, vol. 2020-May, IFAAMAS, pp. 1098-1106, The 19th International Conference on Autonomous Agents and Multi-Agent Systems, Auckland, New Zealand, 9/05/20. <http://www.ifaamas.org/Proceedings/aamas2020/pdfs/p1098.pdf>
De Oliveira Ramos, G., Radulescu, R., Nowe, A., & Tavares, A. (2020). Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences. In B. An, A. El Fallah Seghrouchni, & G. Sukthankar (Eds.), Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020 (pp. 1098-1106). (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2020-May). IFAAMAS. http://www.ifaamas.org/Proceedings/aamas2020/pdfs/p1098.pdf
@inproceedings{13287de77a1743079ac394899c0efb74,
title = "Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences",
abstract = "Multiagent reinforcement learning has shown its potential for tackling real world problems, like traffic. We consider the toll-based route choice problem, where self-interested agents repeatedly commute attempting to minimise their travel costs. In this paper, we introduce Generalised Toll-based Q-learning (GTQ-learning), a multiagent reinforcement learning algorithm capable of realigning agents' heterogeneous preferences over travel time and monetary expenses to obtain a system-efficient equilibrium. GTQ-learning also includes a mechanism to enforce agents to truthfully report their preferences. Our theoretical analysis and empirical results show that GTQ-learning minimises congestion on realistic road networks.",
author = "{De Oliveira Ramos}, Gabriel and Roxana Radulescu and Ann Nowe and Anderson Tavares",
year = "2020",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "IFAAMAS",
pages = "1098--1106",
editor = "Bo An and {El Fallah Seghrouchni}, Amal and Gita Sukthankar",
booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020",
note = "The 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020 ; Conference date: 09-05-2020 Through 13-05-2020",
url = "https://aamas2020.conference.auckland.ac.nz/, https://aamas2020.conference.auckland.ac.nz",
}