INVERSE REINFORCEMENT LEARNING FOR ADAS
Stefano D'Aiuto
INVERSE REINFORCEMENT LEARNING FOR ADAS.
Rel. Stefano Alberto Malan. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2020
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Abstract
This project has been developed in collaboration with my team co-worker Francesco Allegra, a mechatronic engineering student, at Addfor S.p.A., with the guidelines of our academic supervisor Stefano Alberto Malan. The writing of the thesis has been divided as follow: I wrote chapters 2-5-6 while Francesco Allegra wrote chapters 1-3-5 and the results achieved so far have been summarized in “Conclusion”. In this thesis It has been addressed the problem of using Inverse Reinforcement learning algorithms in order to realize autonomous driving’s applications.Among all the possible goals, “autonomous driving” is one of the most challenging, since it is a task performed by a huge amount of people everyday, and that is becoming more and more complex as the number of vehicles grows up during the years.
The complexity of driving lies in the fact that a driving scenario is strongly unpredictable and unstructured, either in highways and especially in a city
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