Theses and Dissertations
Date of Award
7-29-2024
Document Type
Thesis
Degree Name
Master of Science in Engineering (MSE)
Department
Mechanical Engineering
First Advisor
Constantine Tarawneh
Second Advisor
Tohid Sardarmehni
Third Advisor
Horacio Vasquez
Abstract
This thesis introduces an autonomous driving controller designed to replicate individual driving behaviors based on a provided demonstration. The controller employs Inverse Reinforcement Learning (IRL) to formulate the reward function associated with the provided demonstration. IRL is implemented through a dual-feedback loop system. The inner loop utilizes Q-learning, a model-free reinforcement learning technique, to optimize the Hamilton-Jacobi-Bellman (HJB) equation and derive an appropriate control solution. The outer loop leverages this derived control solution to generate parameters for the reward function, which are subsequently integrated into the HJB equation. The ultimate control policy is deduced from the final reward function obtained through IRL. To facilitate the recording of expert demonstrations and the evaluation of the final control policy, the Carla autonomous driving simulator is utilized.
Recommended Citation
Gonzalez Salinas, Rodrigo J., "Personalized Driving Using Inverse Reinforcement Learning" (2024). Theses and Dissertations. 1506.
https://scholarworks.utrgv.edu/etd/1506
Comments
Copyright 2024 Rodrigo J Gonzalez Salinas.
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