Gabriel De Oliveira Ramos, Gabriel De Oliveira Ramos, Roxana Radulescu, Roxana Radulescu, Ann Nowe, Ann Nowe, Anderson Tavares, Anderson Tavares
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",
}