Reinforcement Learning


Stanford School of Engineering



Reinforcement Learning (RL) provides a powerful paradigm for artificial intelligence and the enabling of autonomous systems to learn to make good decisions. RL is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. 

This class will provide a solid introduction to the field of RL. Students will learn about the core challenges and approaches in the field, including generalization and exploration.

Through a combination of lectures, and written and coding assignments, students will become well-versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning-- an extremely promising new area that combines deep learning techniques with reinforcement learning. In addition, students will advance their understanding and the field of RL through an open-ended project. 



Proficiency in Python, CS229 or equivalents or permission of the instructor. Background in linear algebra and basic probability.

Topics include

  • Key features of RL
  • Policy iteration, TD learning and Q-learning
  • Linear value approximation
  • MDP, POMDP, bandit, batch offline and online RL
  • RL algorithms
  • Open challenges and hot topics in RL

Note on Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.