header
Image from OpenLibrary

Autonomous driving using deep reinforcement learning / Mohammed Abdou Tolba ; Supervised Hanan Kamal

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mohammed Abdou Tolba , 2017Description: 61 P. : charts , facsimiles ; 30cmOther title:
  • القياده الذاتيه باستخدام متعلم التقويه العميق [Added title page title]
Subject(s): Available additional physical forms:
  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University -Faculty of Engineering - Department of Electronics and Communications Summary: Autonomous Driving is one of the difficult problems faced the automotive applications. This is due to many corner cases which can be formulated in the unexpected behavior of the autonomous vehicles during the interaction with the other vehicles. The presented work used the Reinforcement Learning field, a strong Artificial Intelligence paradigm that teaches machines through the environment interaction and learning from their mistakes, in order to reach to having an Autonomous Driving vehicle. This work compared between two main categories: Discrete Action Algorithms like: Q-Learning, Double Q-Learning Algorithms, and Continuous Action Algorithms like: Deep Deterministic Policy Gradient (DDPG) Algorithm. It was proven as expected that Continuous Action Algorithms have better performance, so we applied some enhancements over the DDPG algorithm like solving the Long learning time which faced all machine learning problems. These enhancements were depending on disabling some restricted conditions and compensating them with the Reward term. The proposed work depends on Simulator called TORCS to reach to our aim
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2017.Mo.A (Browse shelf(Opens below)) Not for loan 01010110075131000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2017.Mo.A (Browse shelf(Opens below)) 75131.CD Not for loan 01020110075131000

Thesis (M.Sc.) - Cairo University -Faculty of Engineering - Department of Electronics and Communications

Autonomous Driving is one of the difficult problems faced the automotive applications. This is due to many corner cases which can be formulated in the unexpected behavior of the autonomous vehicles during the interaction with the other vehicles. The presented work used the Reinforcement Learning field, a strong Artificial Intelligence paradigm that teaches machines through the environment interaction and learning from their mistakes, in order to reach to having an Autonomous Driving vehicle. This work compared between two main categories: Discrete Action Algorithms like: Q-Learning, Double Q-Learning Algorithms, and Continuous Action Algorithms like: Deep Deterministic Policy Gradient (DDPG) Algorithm. It was proven as expected that Continuous Action Algorithms have better performance, so we applied some enhancements over the DDPG algorithm like solving the Long learning time which faced all machine learning problems. These enhancements were depending on disabling some restricted conditions and compensating them with the Reward term. The proposed work depends on Simulator called TORCS to reach to our aim

Issued also as CD

There are no comments on this title.

to post a comment.