Artificial Intelligence and Robotics

General Objectives. The Reinforcement Learning (RL) course aims to introduce students to fundamental and advanced techniques of RL, a significant area within artificial intelligence and machine learning. Students will gain skills to design and implement algorithms that enable systems to learn and improve autonomously through experience, optimizing their decisions in real-time. Specific Objectives. Students will explore key concepts of RL such as decision policies, Markov Decision Processes, Q-learning, and deep reinforcement learning. They will learn to: Model complex problems using the RL approach. Develop and implement algorithms like Q-learning and Deep Q-Networks (DQN). Apply RL techniques in real-world scenarios like robotics, gaming, etc. Knowledge and Understanding: In-depth knowledge of basic and advanced RL algorithms. Understanding of reward-based learning models and their practical applications. Ability to interpret the results of RL algorithms and evaluate their effectiveness in various contexts. Applying Knowledge and Understanding: Use software frameworks like TensorFlow or PyTorch to implement and test RL algorithms. Analyze current research case studies and projects to understand real-world RL applications. Develop functional prototypes using RL to solve specific problems. Autonomy of Judgment: Students will develop the ability to critically assess RL algorithms, considering their applicability, efficiency, and potential biases. They will also be able to select the most appropriate algorithm for a given problem. Communication Skills: Students will learn to effectively communicate RL concepts, algorithm design decisions, and outcomes to both technical and non-technical audiences using a variety of communication media. Next Study Abilities: This course will prepare students to pursue advanced studies and research in RL, providing the necessary foundation to tackle open problems and innovate in the field. Students will be encouraged to actively contribute to the scientific community through publications, conferences, and collaborations.