Reinforcement Learning - MIT 6.S191 Lecture 5

Explore deep reinforcement learning concepts in this lecture from MIT's Introduction to Deep Learning 6.S191 course, delivered by Alexander Amini. Learn about the fundamentals of reinforcement learning, including how agents learn to make sequential decisions through interaction with environments. The lecture covers key concepts like Markov Decision Processes, Q-learning, policy gradients, and deep reinforcement learning applications. Part of the 2025 Edition of MIT's deep learning curriculum, this comprehensive presentation provides both theoretical foundations and practical insights into this powerful machine learning paradigm. Access additional lectures, slides, and lab materials through the MIT Deep Learning course website.