Paper Info
Title
Minimising Regret in Route Choice.
Abstract
The use of reinforcement learning (RL) in multiagent scenarios is challenging. I consider the route choice problem, where drivers must choose routes that minimise their travel times. Here, selfish RL-agents must adapt to each others' decisions. In this work, I show how the agents can learn (with performance guarantees) by minimising the regret associated with their decisions, thus achieving the User Equilibrium (UE). Considering the UE is inefficient from a global perspective, I also focus on bridging the gap between the UE and the system optimum. In contrast to previous approaches, this work drops any full knowledge assumption.
Year
DOI
Venue
2017
10.5555/3091125.3091470
AAMAS
Keywords
Field
DocType
regret,route choice,multiagent reinforcement learning
Regret,Computer science,Simulation,Bridging (networking),Operations research,Reinforcement learning,Distributed computing
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
1
Name
Order
Citations
PageRank
Gabriel de Oliveira Ramos1144.84