Workbook

Introduction to Modern Reinforcement Learning

An interactive workbook for learning reinforcement learning from foundational MDPs and value functions through deep RL, human feedback, advanced methods, and engineering practice.

Units

  1. 1

    Foundations of Reinforcement Learning

    Build the basic language of reinforcement learning: agents, environments, rewards, Markov decision processes, value functions, Bellman equations, tabular learning, and reward design.

    →
  2. 2

    Deep Reinforcement Learning

    Move from tables to neural function approximation, studying DQN, policy gradients, PPO, exploration, safety, and generalization.

    →
  3. 3

    Human Feedback and Alignment

    Connect reinforcement learning to human preference data, reward modeling, and policy optimization used in aligned AI systems.

    →
  4. 4

    Advanced RL Methods

    Dive into advanced RL techniques: offline learning, model-based agents, world models, hierarchy, inverse RL, and reward inference.

    →
  5. 5

    RL Engineering

    Best practices to ensure your RL results are measurable, reproducible, and comparable across seeds, environments, checkpoints, and evaluation protocols.

    →

About

This workbook was created and is maintained by Dr. Brinnae Bent at Duke University for AIPI 590: Introduction to Modern Reinforcement Learning.

Follow along with the course code demos in GitHub.

This course requires a basic understanding of data science, machine learning, and neural networks. For a refresher on Data Science fundamentals, check out my Data Science Workbook. For a refresher on Deep Learning, check out my Deep Learning Workbook.

Licensed under CC BY-SA 4.0.