Explore deep recurrent Q-networks in this 28-minute lecture, delving into the fundamentals of recurrent neural networks, LSTM units, and their application in Q-learning. Gain insights into the structure and functionality of gates, unrolling techniques, and how these concepts are integrated into recurrent Q networks. Build upon previous knowledge with a comprehensive recap before diving into advanced topics in reinforcement learning and neural network architectures.
Overview
Syllabus
Introduction
Outline
Recap
Recurrent Neural Networks
LST M Unit
Unroll
Gates
Recurrent Q Networks
Q Learning
Taught by
Pascal Poupart
