Reinforcement Learning: An Introduction

Welcome to the documentation page for "Reinforcement Learning: An Introduction". This book is a comprehensive guide to the fundamentals of reinforcement learning, a field of machine learning that focuses on how agents make decisions in an environment to maximize their cumulative reward.

Overview

Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. The agent learns from the consequences of its actions, which are represented by rewards or penalties.

Key Concepts

Agent: The decision-making entity in the environment.
Environment: The surroundings in which the agent operates.
State: The current situation or context in which the agent is.
Action: The decision made by the agent.
Reward: The outcome or consequence of the action taken.

Books

If you're interested in diving deeper into the subject, here are some recommended books:

Reinforcement Learning: An Introduction

Tutorials

For those who prefer a more interactive learning experience, here are some tutorials:

Reinforcement Learning with Python

Images