=Paper=
{{Paper
|id=Vol-3792/invited1
|storemode=property
|title=Reinforcement Learning in Transportation
 (invited paper)
|pdfUrl=https://ceur-ws.org/Vol-3792/invited1.pdf
|volume=Vol-3792
|authors=Ostap Okhrin
|dblpUrl=https://dblp.org/rec/conf/itat/Okhrin24
}}
==Reinforcement Learning in Transportation
 (invited paper)==
<pdf width="1500px">https://ceur-ws.org/Vol-3792/invited1.pdf</pdf>
<pre>
                                Reinforcement Learning in Transportation⋆
                                Ostap Okhrin∗
                                Institute of Transport and Economics, TU Dresden, Germany


                                                                             Abstract
                                                                             Reinforcement learning (RL) has emerged as a powerful method for solving complex control tasks across various domains,
                                                                             from autonomous driving to maritime navigation. Work of my team in RL, particularly in value-based algorithms, addresses
                                                                             critical issues such as overestimation bias, proposing innovative solutions like the T-Estimator (TE) and K-Estimator (KE)
                                                                             for bias control and algorithmic robustness. Our advancements are validated through modifications to Q-Learning and
                                                                             the Bootstrapped Deep Q-Network (BDQN), demonstrating superior performance and convergence. Additionally, we have
                                                                             developed a spatial-temporal recurrent neural network architecture for autonomous ships, enhancing robustness in partial
                                                                             observability and compliance with maritime traffic rules. Our recent endeavors also include a modular framework for
                                                                             autonomous surface vehicles on inland waterways, utilizing DRL agents for path planning and following, significantly
                                                                             outperforming traditional control methods. Moreover, our work on dynamic obstacle avoidance environments for mobile
                                                                             robots and drones emphasizes the importance of controlled training difficulty for better generalization and robustness. This
                                                                             approach has been successfully applied across different platforms, reducing the simulation-to-reality (Sim2Real) gap and
                                                                             improving performance in real-world scenarios. Through these contributions, we aim to advance the practical application
                                                                             and reliability of reinforcement learning in diverse and dynamic environments.




                                ITAT’24: Information technologies – Applications and Theory, Septem-
                                ber 20–24, 2024, Drienica, Slovakia
                                ∗
                                     Corresponding author.
                                Envelope-Open ostap.okhrin@tu-dresden.de (O. Okhrin)
                                                                       © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
                                                                       Attribution 4.0 International (CC BY 4.0).
                                    CEUR
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                                                  ISSN 1613-0073
                                                                       CEUR Workshop Proceedings (CEUR-WS.org)




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