This advanced micro-credential course delves into the cutting-edge realm of reinforcement learning applied to autonomous driving systems. Participants will explore the intersection of AI and transportation, learning how reinforcement learning algorithms enable vehicles to make complex decisions in real-time scenarios. The course covers foundational concepts in reinforcement learning, including Markov decision processes, deep Q-networks, policy gradients, and model-based methods tailored specifically for autonomous driving. Through practical simulations and projects, students will analyze and implement reinforcement learning techniques to train autonomous agents, navigate dynamic environments, and optimize driving behaviors. Emphasis will be placed on understanding safety, ethical considerations, and the challenges of deploying reinforcement learning in the context of autonomous vehicles.
- Teacher: Christos Chronis
- Teacher: Anna-Maria Kaminska
- Teacher: Harokopio University of Athens
- Teacher: Christos Sardianos
- Teacher: Iraklis Varlamis
sensoring, vision, energy storage and movig systems; autonomous robotic platform management etc.
- Teacher: Artem Bronnikov
- Teacher: Kharkiv National University of Radio Electronics
The block aims to provide students with a comprehensive understanding of the European industrial automation landscape, with a particular focus on the challenges posed by Industry 4.0. By studying the integration of automation technologies in the European context, students will gain insights into strategies, policies and best practices that can contribute to sustainable and efficient industrial growth. By understanding the technological fundamentals, opportunities and barriers, students will be better prepared to promote the adoption of automation technologies in a sustainable and efficient manner.
- Teacher: Anna-Maria Kaminska
- Teacher: Serhii Ruban
- Teacher: Kryvyi Rih National University