Eric Sturzinger

Papers

Total Citations

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About

Eric Sturzinger is a researcher focused on the intersection of reinforcement learning and autonomous systems, with a particular emphasis on efficient, real-world navigation. His most cited work, "Autonomous Navigation via a Deep Q Network with One-Hot Image Encoding" (2019), investigates the feasibility of using a Deep Q-Network (DQN) for autonomous driving, proposing a novel one-hot image encoding technique to simplify state representation. This approach demonstrates how reinforcement learning models can be adapted for unique road and traffic conditions without relying on complex convolutional networks, offering a streamlined path toward safer autonomous navigation. While his citation count reflects the niche, emerging nature of his research, Sturzinger’s work contributes to the foundational understanding of how RL can be applied to real-world control problems. His research is particularly relevant for students and engineers exploring lightweight, learning-based solutions for robotics and autonomous vehicles, bridging the gap between theoretical RL and practical deployment.