Virtual Platform for Reinforcement Learning Research for Heavy Vehicles
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Typ
Examensarbete på kandidatnivå
Program
Publicerad
2020
Författare
Emvin, Carl
Persson, Jesper
Åkvist, William
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The main objective of the project, tasked by Volvo Group Trucks Technology, is to
implement a reinforcement learning agent in a open-source vehicle simulation model
known as VehProp, developed at Chalmers University of Technology in MathWorks
Simulink. The project also aims to improve VehProp, particularly the Equivalent
Consumption Minimization Strategy. As a proof of concept for the reinforcement
learning implementation, an agent is trained to control the brakes of a hybrid electric
heavy duty vehicle in order to minimize fuel consumption through regenerative
braking. Much effort is put in the theory chapter, explaining reinforcement learning
and the simulation platform. The reinforcement learning agent is successfully implemented
in the simulation platform, using the Reinforcement Learning Toolbox in
Matlab. The training of the agent to control the brakes of a hybrid electric heavy
duty vehicle was unsuccessful, with the agent failing to display the wanted behavior.
Suggestions for future work with the agent are presented, mainly fulfilling the
Markov property, investigating sparse reward functions and the general implementation
of the agent to assure action-reward causality. Improvements to the Equivalent
Consumption Minimization Strategy of the simulation platform were made with a
decrease in fuel consumption as a result.
Beskrivning
Ämne/nyckelord
reinforcement learning , vehicle simulation , VehProp , hybrid electric vehicle