Reinforcement learning

Reinforcement learning

Brunton Lab, University of Washington

Reinforcement learning is a powerful technique at the intersection of machine learning and control theory, and it is inspired by how biological systems learn to interact with their environment.

Follow Steven Brunton:
On Twitter: @eigensteve
On YouTube: @Eigensteve

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Brunton Lab
Brunton Lab
Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming
Jan 2022
University of Washington

Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming

Steven L. Brunton, University of Washington
Overview of Deep Reinforcement Learning Methods
Jan 2022
University of Washington

Overview of Deep Reinforcement Learning Methods

Steven L. Brunton, University of Washington
Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning
Jan 2022
University of Washington

Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning

Steven L. Brunton, University of Washington
Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming
Jan 2022
University of Washington

Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming

Steven L. Brunton, University of Washington
Overview of Methods
Jan 2022
University of Washington

Overview of Methods

Steven L. Brunton, University of Washington
Deep Reinforcement Learning for Fluid Dynamics and Control
Mar 2021
University of Washington

Deep Reinforcement Learning for Fluid Dynamics and Control

Steven L. Brunton, University of Washington
Neural Networks for Learning Control Laws
In preparation
University of Washington

Neural Networks for Learning Control Laws

Steven L. Brunton, University of Washington
Machine Learning Meets Control Theory
In preparation
University of Washington

Machine Learning Meets Control Theory

Steven L. Brunton, University of Washington