Reinforcement Learning: An Introduction

Sutton, Richard S.

Reinforcement Learning: An Introduction - 2nd ed - USA MIT Press 2018 - 526p

Introduction
Part 1: Tabular Solution Methods
Multi Armed Bandits
Finite Markov Decision Processes
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning
N-Step Bootstrapping
Planning and Learning with Tabular Methods
Part 2: Approximate Solution Methods
On-Policy Prediction with Approximation
On-Policy Control with Approximation
Off-Policy Methods with Approximation
Eligibility Traces
Policy Gradient Methods
Part 3: Looking Deeper
Psychology
Neuroscience
Applications and Case Studies
Frontiers


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