Deep Reinforcement Learning Plaat Aske
Contents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 What is Deep Reinforcement Learning? . . . . . . . . . . . . . . .…
Specifikacia Deep Reinforcement Learning Plaat Aske
Contents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 What is Deep Reinforcement Learning? . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Three Machine Learning Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.3 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Tabular Value-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.1 Sequential Decision Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.2 Tabular Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.3 Classic Gym Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.4 Summary and Further Reading . . . . . . . . . . . . . . . . .
Learning Deep Learning
Deep Learning
Generative Deep Learning
Deep Learning from Scratch
Deep Learning with PyTorch
Fundamentals of Deep Learning
Advanced Applied Deep Learning
Dive Into Deep Learning
Deep Learning in Science
Math for Deep Learning
Dive into Deep Learning