MMI706 REINFORCEMENT LEARNING
|METU Credit (Theoretical-Laboratory hours/week):
|Language of Instruction:
|Level of Study:
|Fall and Spring Semesters.
This course aims to give background knowledge on several topics related to reinforcement learning and provide an environment for practical applications. Multi-armed Bandits, Monte Carlo methods, Markov Decision Processes, Dynamic Programming and Temporal-Difference Learning are some of the core topics that will be covered through lectures. The course aims to balance theory and practice in that it will involve students implementing all of the described algorithms, testing those algorithms in different game environments, and reading recent research papers on the reinforcement learning field.