Temple University 3 credits CIS
This course provides a comprehensive introduction to reinforcement learning (RL), a branch of machine learning that focuses on how agents learn to make optimal decisions through interaction with their environment. Students will develop both theoretical understanding and practical skills in designing and implementing RL algorithms. The course begins with foundational concepts, including Markov Decision Processes and dynamic programming solutions, then progresses through model-free prediction methods and function approximation techniques essential for handling real-world problems. Advanced topics include temporal difference learning algorithms, policy gradient methods, and the critical distinctions between on-policy and off-policy learning approaches. The course concludes with an exploration of strategies and inverse reinforcement learning, connecting classical RL to modern AI systems.
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