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DTSTART:20190310T070000
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DTSTART:20191103T060000
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DTSTART;TZID=America/Toronto:20200121T140000
SEQUENCE:0
TRANSP:TRANSPARENT
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URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-closing-modelling-gap-transfer
LOCATION:E7 - Engineering 7 200 University Ave West 5419 Waterloo ON N2L 3G
 1 Canada
SUMMARY:Master’s Thesis Presentation: Closing the Modelling Gap: Transfer
 \nLearning from a Low-Fidelity Simulator for Autonomous Driving
CLASS:PUBLIC
DESCRIPTION:ARAVIND BALAKRISHNAN\, MASTER’S CANDIDATE\n_David R. Cheriton
  School of Computer Science_\n\nThe behaviour planning subsystem\, which i
 s responsible for high-level\ndecision making and planning\, is an importa
 nt aspect of an autonomous\ndriving system. There are advantages to using 
 a learned behaviour\nplanning system instead of traditional rule-based app
 roaches. However\,\nhigh quality labelled data for training behaviour plan
 ning models is\nhard to acquire. Thus\, reinforcement learning (RL)\, whic
 h can learn a\npolicy from simulations\, is a viable option for this probl
 em.
DTSTAMP:20260311T054337Z
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