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Webinar 9: Reinforcement Learning

The session is designed to provide a comprehensive overview of RL, understanding its core concepts, key algorithms, practical applications, and the challenges it faces. We begin by defining RL and distinguishing it from other types of machine learning, namely supervised and unsupervised learning. The session includes a detailed explanation of Markov Decision Processes (MDPs), which serve as the mathematical framework for RL, highlighting states, actions, transition probabilities, and rewards. The concepts of policies, value functions, and the exploration-exploitation trade-off are also examined to give participants a robust understanding of how RL agents learn and make decisions. The core of the session focuses on key RL algorithms, starting with classical methods such as Dynamic Programming, including policy iteration and value iteration. We then explore Monte Carlo methods and Temporal Difference learning, with specific emphasis on Q-Learning and SARSA. Advanced methods such as Deep Q-Networks (DQN) method is also covered, illustrating how deep learning integrates with RL to solve complex problems. 

Abebaw Degu is an engineer, teacher, and researcher. He holds BSc in software engineering and MSc in Computer science and engineering from Adama science and Technology University, and a PhD in Artificial Intelligence from Euromed University of Fes. Abebaw’s research focuses on reinforcement learning and transfer learning, particularly in the areas of sequential decision-making and optimization. He is actively engaged in innovative research and has published papers and presented his work in international conference in these fields.