Reinforcement Learning

Course content

This proposed course provides a unique opportunity for students explore the entire spectrum of reinforcement learning, beginning with foundational principles and progressing to advanced concepts and the latest innovations. Students will have the chance to apply these theories and techniques to practical, real-world challenges, gaining both a deep theoretical understanding and hands-on experience. Topics include model based reinforcement learning (RL); model free concepts; hierarchical methods; imitation learning; inverse RL; reward shaping; policy gradient methods; multitask learning and transfer; deep RL; multi-agent RL; and applications of the presented models and algorithms.