Play all

Reinforcement Learning: Machine Learning Meets Control Theory

Reinforcement Learning Series: Overview of Methods

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

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

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

Deep Reinforcement Learning: Neural Networks for Learning Control Laws

Overview of Deep Reinforcement Learning Methods

Deep Reinforcement Learning for Fluid Dynamics and Control

Description:

Explore the intersection of machine learning and control theory in this comprehensive video series on reinforcement learning. Delve into various methods, including model-based approaches like policy iteration, value iteration, and dynamic programming. Learn about model-free techniques such as Q-learning and temporal difference learning. Discover the principles of nonlinear control through Hamilton Jacobi Bellman equations and dynamic programming. Investigate deep reinforcement learning, focusing on neural networks for learning control laws and their applications in fluid dynamics and control. Gain a thorough understanding of both traditional and cutting-edge reinforcement learning techniques over the course of 3 hours and 30 minutes.

Reinforcement Learning

Steve Brunton

Add to list

#Computer Science #Machine Learning #Reinforcement Learning #Artificial Intelligence #Neural Networks #Science #Physics #Fluid Mechanics #Fluid Dynamics #Engineering #Control Theory #Deep Reinforcement Learning #Q-learning #Model Based Reinforcement Learning

0:00 / 0:00