Online Learning for Energy Efficient Navigation using Contextual Information
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
YUNATCI, YONCA
BATIKAN UNAL, AHMET
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Accurately predicting the energy consumption of road segments is an important
topic in electric vehicles that might alleviate the range concerns if it is addressed
properly. We employ a contextual combinatorial multi-armed bandit framework to
learn the unknown parameters of an energy consumption model which is necessary
for energy-efficient navigation. Four different agents: Thompson Sampling, Disjoint
LinUCB, Hybrid LinUCB, and greedy algorithms are implemented to observe their
performance. All experiments are conducted on the output of a Luxembourg SUMO
traffic simulation. The main finding of this research is that contextual information
such as speed and acceleration data contributes to better learning of parameters.
Although the contextual combinatorial algorithms seem promising for addressing
the energy-efficient shortest path problem, none of the agents achieve zero regret
consistently which indicates that further improvements are necessary to obtain the
desired results.
Beskrivning
Ämne/nyckelord
Contextual combinatorial multi-armed bandit , online learning , electric vehicles , energy consumption prediction , computer science