Reinforcement Learning Course
How do AI teams use RL in production?
RL Part 13: An exploration of real-world RL case studies.
Reinforcement Learning Course
A collection of 14 posts
Reinforcement Learning Course
Agentic RL: Environments, Trajectories, and the Training
RL Part 12: From a single judged group to a full multi-step training loop with ART and RULER.
Reinforcement Learning Course
The Reward Signal Problem for Agents
RL Part 11: From verifiable rewards to LLM-as-a-judge.
Reinforcement Learning Course
Verifiable Rewards and GRPO
RL Part 10: Dropping two models from the four-model pipeline, and building rewards you can trust.
Reinforcement Learning Course
RLHF: Aligning Language Models with Human Feedback
Part 9: From human preferences to a trained reward signal, and the four-model PPO pipeline.
Reinforcement Learning Course
Proximal Policy Optimization
RL Part 8: Trust regions, the clipped surrogate, and the workhorse of modern RL.
Reinforcement Learning Course
Policy Gradients: REINFORCE and Actor-Critic
RL Part 7: Learning the policy directly, from REINFORCE to actor-critic.
Reinforcement Learning Course
Introduction to Deep RL and DQN
RL Part 6: From linear features to neural networks, and the engineering choices that makes deep value-based RL possible.
Reinforcement Learning Course
Function Approximation
RL Part 5: From tables to parameterized value functions.
Reinforcement Learning Course
Model-Free Learning
RL Part 4: Learning value functions and policies without a model. Monte Carlo methods, TD(0), SARSA, Q-learning, and the bias-variance bridge between them.
Reinforcement Learning Course
Bellman Equations and Dynamic Programming
RL Part 3: Bellman expectation and optimality equations, policy iteration, value iteration, and why dynamic programming needs a model.
Reinforcement Learning Course
Markov Decision Processes and Value Functions
RL Part 2: Markov decision processes, returns, policies, and value functions.
Reinforcement Learning Course
Foundations of Reinforcement Learning
RL Part 1: Agents, environments, rewards, and why RL is different from supervised learning.
Reinforcement Learning Course
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.
