Reinforcement Learning

This course on Reinforcement Learning aims to teach students the fundamentals of deep reinforcement learning. The learning outcomes include understanding the Q function, Deep Q Networks, policy learning algorithms, and real-life applications of reinforcement learning. The course covers topics such as discrete vs continuous actions, training policy gradients, and explores case studies like Atari results, AlphaGo, and AlphaZero. The teaching method involves lectures with slides and lab materials. This course is intended for individuals interested in deep learning, specifically in the field of reinforcement learning.