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Reinforcement Learning

Reinforcement Learning Course

A series of technical deep dives on Reinforcement Learning that covers fundamentals and background, the classical techniques, MDPs, Bellman equations, deep RL methods, how RL is used to train modern language models, agentic RL, and much more.

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Course content

  1. Foundations of Reinforcement Learning
  2. Markov Decision Processes and Value Functions
  3. Bellman Equations and Dynamic Programming
  4. Model-Free Learning
  5. Function Approximation
  6. Introduction to Deep RL and DQN
  7. Policy Gradients: REINFORCE and Actor-Critic
  8. Proximal Policy Optimization
Published on Apr 25, 2026