Explore partial observable reinforcement learning in this 29-minute lecture from the CS885 course at the University of Waterloo. Delve into topics such as Partial Observable Markov Decisions, Hidden Markov Models, and Deep Recurrent Q Networks. Learn about belief monitoring, recurrent neural networks, and their applications in speech recognition. Gain insights into Markovian processes and long short-term memory networks. Access accompanying slides on the course website for a comprehensive understanding of this advanced machine learning concept.
Overview
Syllabus
Introduction
Partial Observable Markov Decisions
Reinforcement Learning Recap
Markovian Process
Hidden Markov Model
Speech Recognition
Markov Decision Processes
Belief Monitoring
Recurrent Neural Networks
Longshore Term Memory Networks
Deep Recurrent Q Network
Taught by
Pascal Poupart
