Deep Reinforcement Learning

3 units

Deep Reinforcement Learning (RL) is a cutting-edge field at the intersection of artificial intelligence and decision-making. This course provides an in-depth exploration of the fundamental principles, algorithms, and applications of deep reinforcement learning. It covers basic algorithms, including value-based, policy-based, actor-critic, and model-based methods, as well as advanced topics, such as meta RL, offline RL, and multi-agent RL. By combining deep learning techniques with reinforcement learning, this course equips students with the necessary skills to build intelligent systems that can learn from experiences and make optimal decisions in complex environments. Evaluation of this course is based on written and programming assignments, and a final course project.