Reinforcement Learning

Learning by Trial, Error, and Reward

This intermediate specialization gets into the mechanics of reinforcement learning — the branch of machine learning behind agents that learn optimal behavior through reward and feedback rather than labeled examples, including the Markov models that underpin it.

Who it's for

Learners with some machine learning background who want to move into agentic systems and decision-intelligence problems, where an algorithm has to act, not just predict.

What you'll implement

• Core reinforcement learning algorithms and Markov decision processes
• A complete RL solution built from the ground up
• Applying RL and agentic thinking to real-world decision problems

Over 66,000 learners have worked through it, making it a proven route into one of the more conceptually demanding — and currently in-demand — corners of applied AI.