Reinforcement Learning

This course on Reinforcement Learning aims to teach students the fundamentals of deep reinforcement learning. By the end of the course, learners will understand 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 showcases examples like the VISTA simulator and AlphaGo. The teaching method includes lectures with slides and lab materials. This course is intended for individuals interested in deep learning and reinforcement learning concepts.