Date of Award
Fall 10-24-2025
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
November 2025
Department
Computer and Electrical Engineering and Computer Science
College Granting Degree
College of Engineering and Computer Science
Degree Name
Doctor of Philosophy (PhD)
Thesis/Dissertation Advisor [Chair]
Zhen Ni
Abstract
With the rapid progress of reinforcement learning (RL) and its applications in robotics, autonomous driving, and energy systems, learning reliable reward functions has become a central challenge. Traditional RL relies heavily on handcrafted reward functions, which are often infeasible to design in complex real-world environments. Inverse reinforcement learning (IRL) provides an alternative by recovering reward functions from expert demonstrations. However, existing IRL methods suffer from inefficiency, poor robustness under perturbations, and limited generalization to unseen scenarios.
This dissertation focuses on advancing IRL from three complementary perspectives: computational efficiency, robustness, and generalization. Specifically, the following problems are investigated: 1. Computational Efficiency: We develop a novel, efficient IRL (e-IRL) framework that reformulates reward recovery through feature expectation matching, eliminating the need for state visitation estimation. This reduces memory and computation costs while scaling effectively to high-dimensional environments. 2. Robustness: To address perturbed environments, we introduce a multi-virtual-agent IRL (MVIRL) framework. By training multiple agents across parallel perturbed environments with weight-sharing and data aggregation, MVIRL learns robust reward functions that remain stable under noise, gravity variations, and adversarial conditions. 3. Generalization and Personalization: We propose a contrastive IRL (CIRL) framework that integrates self-supervised contrastive representation learning with maximum entropy IRL. CIRL leverages momentum encoders and reward-regularized contrastive loss to improve sample efficiency and adapt to personalized driving styles, ensuring both robustness and adaptability.
Recommended Citation
Lin, Yanbin, "EFFICIENT AND ROBUST INVERSE REINFORCEMENT LEARNING FOR COMPLEX AND DYNAMIC ENVIRONMENTS" (2025). Electronic Theses and Dissertations. 203.
https://digitalcommons.fau.edu/etd_general/203