“Applied Reinforcement Learning” is a practical guide that helps professionals understand how reinforcement learning and modern AI approaches can enhance the efficiency of business processes. The material is focused on the application of RL in real operational tasks: from logistics optimization and dynamic pricing to improving recommendations and tuning AI models through RLHF.
What this book offers
The course sequentially reveals how to use RL methods to improve decision quality, automate routine processes, and enhance the performance of digital products. The main focus is on real business cases, repeatable experiments, and practical benefits.
Key Advantages
- applied focus: studying RL on real company tasks;
- accessible explanations without complex mathematics;
- step-by-step examples with code and visualizations;
- support for modern AI approaches — LLM integration, RLHF, simulation environments;
- result-oriented: from task setting to implementation and evaluation.
Topics and algorithms covered in the book
The material covers both basic algorithms and advanced deep learning methods in the context of RL.
Fundamentals of RL
- contextual bandits and action selection tasks;
- tabular RL and classic approaches;
- value-based methods, including Deep Q-Networks (DQN);
- actor-critic algorithms;
- Deep Deterministic Policy Gradient (DDPG) for continuous actions.
Working with Simulations
A separate section is devoted to creating custom simulation environments and modeling business processes — a key skill for successfully applying RL in companies.
Practical Industry Cases
Each chapter is a complete project where the reader acts as an expert and step-by-step implements a solution based on RL.
Examples of Tasks
- optimization of supply chains and inventory management;
- improvement of delivery logistics and route planning;
- dynamic pricing and revenue growth in e-commerce;
- optimization of advertising campaigns and budgets;
- training AI chatbots and RLHF integration.
Who the book is for
The material is aimed at professionals familiar with business processes and possessing basic programming skills.
Who will benefit
- developers and engineers;
- analysts and data scientists;
- ML engineers and MLOps specialists;
- technical team and product leaders;
- anyone who wants to apply reinforcement learning to real business tasks.
