Reinforcement Learning and RLHF: Training and Aligning AI Models

A premium, advanced and hands-on course on reinforcement learning and RLHF, fully updated for 2026 and expanded to university depth. You will start from the mathematical foundations — Markov decision processes, rewards, policies, value functions, the Bellman equations and dynamic programming — and work through the exploration-exploitation trade-off, Monte Carlo and temporal-difference learning, Q-learning, function approximation and the deadly triad, Deep Q-Networks and their modern refinements (Double DQN, dueling architectures, prioritized replay). From there you will master modern policy optimization: REINFORCE, actor-critic (A2C/A3C), continuous control with DDPG, TD3 and Soft Actor-Critic, 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 techniques that made modern assistants possible: preference data collection and its legal obligations, reward models trained on human preferences, PPO-based RLHF as used to align systems such as ChatGPT and Claude, and the newer methods — Direct Preference Optimization (DPO), GRPO, RLAIF, Constitutional AI, and reinforcement learning with verifiable rewards (RLVR), the engine behind 2026 reasoning models. You will learn to spot and prevent reward hacking, reason about alignment and safety, evaluate aligned models honestly, and build real reward-model and DPO pipelines with Hugging Face TRL. Every concept is paired with correct, runnable Python and PyTorch code, worked numeric examples and exercises, and the course keeps a firm focus on the legal, licensing and ethical obligations around preference data and alignment. Includes a comprehensive final assessment.