Introduction to Reinforcement Learning: From Trial and Error to Intelligent Agents
The fundamental concepts of reinforcement learning — agents, environments, rewards and policies — with the mathematics and a working Q-learning imp...
Hadi Aghazadeh
Decision-making under uncertainty — reinforcement learning, optimization and forecasting
Hadi Aghazadeh writes about decision-making under uncertainty: reinforcement learning, optimization and forecasting in production. Senior Data Scientist at Enverus, PhD candidate at the University of Calgary, and author of Applied Reinforcement Learning (Manning).
Applied Reinforcement Learning
Business optimization and LLM fine-tuning · Manning Publications
Most reinforcement learning material stops at games and simulations. This book is about the other part: framing a real business problem as a sequential decision problem, building an environment that respects its constraints, and getting a policy into production that people will actually trust. Warehouse logistics and dynamic pricing through to fine-tuning language models with PPO and GRPO.
In Manning Early Access Program — full release expected January 2027.
Recent Posts
What I write about
Reinforcement learning
Framing business problems as sequential decisions — pricing, routing, allocation — and what breaks between a working policy and a deployed one.
Optimization & operations research
Mixed-integer programming, branch-and-bound and cutting planes, and where learned methods beat exact ones — or guide them.
Forecasting & ML systems
Time-series forecasting at scale, backtesting that means something, and the production plumbing that keeps a model trustworthy.
LLMs & AI engineering
Retrieval systems, agentic workflows, and fine-tuning with PPO, GRPO and verifiable rewards.