ABSTRACT
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amo
TABLE OF CONTENTS
part 1|2 pages
Part I: Introduction
part 2|2 pages
Part II: Model-Free Policy Iteration
part 3|2 pages
Part III: Model-Free Policy Search
part 4|2 pages
Part IV: Model-Based Reinforcement Learning