Name
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.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.5555/3398761.3398889 | AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems
Auckland
New Zealand
May, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7518-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel de Oliveira Ramos | 1 | 14 | 4.84 |
| Roxana Rădulescu | 2 | 2 | 2.41 |
| Ann Nowé | 3 | 971 | 123.04 |
| Anderson R. Tavares | 4 | 0 | 0.34 |