Reinforcement Learning and RLHF: Training and Aligning AI Models

A premium, advanced and hands-on course on reinforcement learning and RLHF, updated for 2026. You will start from the mathematical foundations — Markov decision processes, rewards, policies, value functions and the Bellman equations — and work through the exploration-exploitation trade-off, Monte Carlo and temporal-difference learning, Q-learning and Deep Q-Networks. From there you will master modern policy optimization: REINFORCE, actor-critic (A2C/A3C), and Proximal Policy Optimization (PPO) in full detail, with real code on Gymnasium and Stable-Baselines3. The second half of the course turns to the technique that made modern assistants possible: RLHF. You will understand how supervised fine-tuning, reward models trained on human preferences, and PPO combine to align large language models such as those behind ChatGPT and Claude, then move to the newer, simpler alternatives — Direct Preference Optimization (DPO), GRPO, RLAIF and Constitutional AI. You will learn to spot and prevent reward hacking, reason about alignment and safety, and build real reward-model and DPO pipelines with Hugging Face TRL. Every concept is paired with correct, runnable Python and PyTorch code, and the course keeps a firm focus on the legal, licensing and ethical obligations around preference data and alignment. Includes a comprehensive final assessment.