Reinforcement Learning for Business

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  • MEAP began September 2025
  • Last updated September 2025
  • Publication in Summer 2026 (estimated)
  • ISBN 9781633434844
  • 375 pages (estimated)
  • printed in black & white

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Optimize business processes, people, and resources using AI and the power of reinforcement learning.

Whether you’re finding the best delivery route, establishing efficient schedules, or maximizing profit with dynamic pricing, success in business can come down to the right optimizations. AI tools like large language models can help. When you tune them to your specific business data, they really start to shine! Reinforcement Learning for Business teaches the essentials of business optimization using reinforcement learning and AI models through relevant and useful business applications. You’ll apply RL to supply chains, marketing and ad campaigns, logistics, and even optimizing AI chatbots. Graphics, code samples, and math-lite explanations demonstrate the core theories of RL in an intuitive and illustrative way.

In Reinforcement Learning for Business you’ll learn:

  • RL for real-world challenges like scheduling, routing, and pricing
  • Custom simulation environments to train RL agents
  • RL algorithms including contextual bandits, Deep Q-Networks and actor-critic methods
  • End-to-end problems for e-commerce, vehicle routing, and supply chain management
  • Integrate RL with Large Language Models using RLHF

Reinforcement Learning models develop through trial and error, exploring their environment and learning from successes and mistakes. This powerful AI approach can easily be turned to automatically optimizing business processes like pricing, logistics, and customer engagement. In Reinforcement Learning for Business you’ll bring RL to solve common yet practical industry challenges. You’ll discover both the algorithms that underpin RL, and how to build the simulation environments you’ll need to train custom models.

about the book

Reinforcement Learning for Business presents RL in an intuitive way, effectively applying this powerful technique in real-world environments. Each chapter explores an end-to-end industry case study—including optimizing an ad campaign using contextual bandit algorithms, production line scheduling problems using tabular RL and Deep Q-Networks for real-world business challenges, and applying dynamic pricing with Deep Deterministic Policy Gradient for solving dynamic pricing problems. For each example, you’ll step into the role of a consultant, analyzing how a problem can be effectively solved with RL. You’ll discover full coverage of the latest and most-relevant techniques for RL, including utilizing Reinforcement Learning with Human Feedback (RLHF) to align Large Language Models into business objectives and constraints.

about the reader

For readers comfortable with business processes and intermediate level programming. No advanced math or specialist AI knowledge is required.

about the author

Hadi Aghazadeh is a Machine Learning Engineer at Bits in Glass, where he applies advanced AI and Generative AI solutions to real-world business challenges. He has delivered numerous high-impact projects—from dynamic pricing in ride-hailing to fraud detection in energy and banking. Hadi has earned multiple awards, including first place in the Alberta Machine Intelligence Institute Reinforcement Learning Competition and the prestigious Alberta Innovates Scholarship.
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choose your plan

team

monthly
annual
$49.99
$399.99
only $33.33 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free product every time you renew
  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • renews monthly, pause or cancel renewal anytime
  • renews annually, pause or cancel renewal anytime
  • Reinforcement Learning for Business ebook for free