To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it 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 reinforcement learning and students will learn about the core challenges and approaches, 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 a final project.
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 – Introduction
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 – Given a Model of the World
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3 – Model-Free Policy Evaluation
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 4 – Model-Free Control
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 5 – Value Function Approximation
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6 – CNNs and Deep Q Learning
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 – Imitation Learning
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 8 – Policy Gradient I
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 9 – Policy Gradient II
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 10 – Policy Gradient III & Review
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 11 – Fast Reinforcement Learning
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 12 – Fast Reinforcement Learning II
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 13 – Fast Reinforcement Learning III
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 – Batch Reinforcement Learning
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 16 – Monte Carlo Tree Search
Source: http://onlinehub.stanford.edu/cs234
Emma Brunskill is an Assistant Professor in the Department of Computer Science. She is affiliated with the Stanford Artificial Intelligence Laboratory and the Stanford Statistical Machine Learning Group. Brunskill’s research centers on reinforcement learning in high stakes scenarios.
Reinforcement Learning, Interactive Machine Learning, ML/AI for Education at Stanford University, Assistant Professor, Computer Science at Stanford University, Assistant Professor, Computer Science at Carnegie Mellon University from 2011-2017, NSF Mathematical Science Postdoctoral Fellow, Computer Science Dept. at the University of California Berkeley from 2009-2011.
