Reinforcement Learning: An Introduction

Provides a canonical overview or reference point for the relevant DoOperator research area.

The standard textbook introduction to reinforcement learning, covering MDPs, value functions, temporal-difference learning, policy gradients, and core algorithms.

Use when orienting a new paper, blog post, benchmark, or research plan in this area.

Do not cite the overview as evidence that a specific method works in a specific deployment setting without checking the underlying primary paper.