Reinforcement Learning Series: Overview of Methods
This video introduces the variety of methods for model-based and model-free reinforcement learning, including: dynamic programming, value and policy iteration, Q-learning, deep RL, TD-learning, SARSA, policy gradient optimization, among others.
This is the overview in a series on reinforcement learning, following the new Chapter 11 from the 2nd edition of our book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Book Website: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf
RL Chapter: https://faculty.washington.edu/sbrunton/databookRL.pdf
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/
Brunton Website: eigensteve.com
This video introduces the variety of methods for model-based and model-free reinforcement learning, including: dynamic programming, value and policy iteration, Q-learning, deep RL, TD-learning, SARSA, policy gradient optimization, among others.
This is the overview in a series on reinforcement learning, following the new Chapter 11 from the 2nd edition of our book “Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control” by Brunton and Kutz
Book Website: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf
RL Chapter: https://faculty.washington.edu/sbrunton/databookRL.pdf
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/
Brunton Website: eigensteve.com
