DEEP Reinforcement Learning

Class overview

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research is at the forefront of machine learning. Deep RL is able to solve a wide range of complex decision-making tasks, opening up new opportunities in domains such as healthcare, robotics, smart grids, finance, and many more. This class will cover recent advances in deep RL, including imitation learning, Policy Gradients, Deep Q-learning, Actor-Critic algorithms, and model-based RL. The course will be a combination of lectures, student presentations, and projects.

Lecturer: Qi (Rose) Yu (



Class Assessment

  • 30 % homework (10% x 3)

  • 50 % project

    • 5 % proposal

    • 15 % milestone report

    • 20 % final report

    • 10 % final presentation

  • 15 % paper discussion

  • 5 % lecture scribe



Q: What are the pre-requisites?

  • CSE 150b (or equivalent) and CSE 151a (or equivalent) or CSE 250a

  • CSE 151b (or equivalent)

  • Proficiency in Python.

Q: Can first year undergraduates take this course?

  • Restricted to students with sophomore, junior, or senior standing within the CS25, CS26, CS27, CS28, EC26, and DS25 majors.

  • All other students will be allowed as space permits.

About me

My Chinese name is Qi Yu. That is also the instructor name in the registrar's office. I publish under the name Rose Yu. You can learn more about my research at my personal website.