Overview
Deep Reinforcement Learning (RL) has made massive strides in the last decade for sequential decision making problems such as playing Atari games, mastering GO, and continuous control of robots. This course serves as a graduate-level introduction to RL, with an emphasis on applications and recent research. Students will be introduced to a broad set of topics in RL: Basic formalisms; Exploration vs exploitation; Imitation learning; Model-free RL; Model-based control and planning; Unsupervised learning for RL; Applications to games, robotics, industry; Current frontiers. This course will involve several coding home-works where you will implement various algorithms, and a final project. Other alternative titles for this course are Adaptive control and learning, Dynamic optimization.
