Reward and Learn: Practical Reinforcement Learning for Autonomous Agents, Games, and Robot Control

By: Boozman, Richard

Train intelligent systems that learn from interaction, adapt to environments, and improve over time

Some systems are programmed.
Others learn.

Reinforcement learning enables machines to make decisions, learn from experience, and improve through feedback. It powers everything from game playing AI to robotics and autonomous control.

"Reward and Learn" is a practical, hands on guide to building reinforcement learning systems using Python and modern ML frameworks such as PyTorch.

This book focuses on real implementation, helping you move from theory to working intelligent agents.

Why reinforcement learning matters

Reinforcement learning is the foundation of decision making AI.

With the right approach, you can build systems that:

learn optimal actions through trial and erroradapt to changing environmentsmaximize long term rewardscontrol complex systemsdevelop intelligent strategies

This book shows you how to build these systems step by step.

What you will learnfundamentals of reinforcement learningagents, environments, states, and rewardsvalue based and policy based methodsQ learning and deep Q networkspolicy gradients and actor critic methodstraining agents in simulated environmentsreward design and optimizationexploration vs exploitation strategiesscaling reinforcement learning systemsapplying RL to robotics and control
From algorithms to intelligent agents

Throughout the book, you will learn how to:

build RL agents from scratchtrain agents to solve tasks and gamesdesign effective reward systemsapply deep learning to RL problemsdebug and improve agent performancedeploy RL systems in real applications

Each chapter is designed to produce working results.

Practical applicationsgame playing AI agentsautonomous robotics controlrecommendation systemsresource optimization systemssimulation based learningintelligent decision making systems

These examples reflect real world applications of RL.

Who this book is formachine learning engineersAI developersdata scientistsrobotics engineersdevelopers interested in intelligent systems

If you want to build systems that learn from experience and adapt intelligently, this book provides the roadmap.

Learn from feedback.
Optimize decisions.
Build intelligent agents.

• Format: Paperback
• Language: English
• ISBN: B0GYZCX6KT
• ISBN13: 9798257909924
• Release Date: May 2026
• Publisher: Independently Published
• Length: 286 Pages
• Weight: 0.85 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"