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

Name: Reward and Learn: Practical Reinforcement Learning for Autonomous Agents, Games, and Robot Control
Brand: Independently Published
SKU: 9798257909924
Price: 2093.0 INR
Availability: InStock

by Richard Boozman

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ISBN13: 9798257909924
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: May 5, 2026
Pages: 286
Original Price: GBP 18.46
Language: English
Edition: N/A
Item Weight: 386 grams
BISAC Subject(s): Artificial Intelligence / Generative AI

About The Book

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 error
adapt to changing environments
maximize long term rewards
control complex systems
develop intelligent strategies

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

What you will learn

fundamentals of reinforcement learning
agents, environments, states, and rewards
value based and policy based methods
Q learning and deep Q networks
policy gradients and actor critic methods
training agents in simulated environments
reward design and optimization
exploration vs exploitation strategies
scaling reinforcement learning systems
applying RL to robotics and control

From algorithms to intelligent agents

Throughout the book, you will learn how to:

build RL agents from scratch
train agents to solve tasks and games
design effective reward systems
apply deep learning to RL problems
debug and improve agent performance
deploy RL systems in real applications

Each chapter is designed to produce working results.

Practical applications

game playing AI agents
autonomous robotics control
recommendation systems
resource optimization systems
simulation based learning
intelligent decision making systems

These examples reflect real world applications of RL.

Who this book is for

machine learning engineers
AI developers
data scientists
robotics engineers
developers 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.

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