AI engineering glossary · Reinforcement learning
What is Monte Carlo methods (reinforcement learning)?
Monte Carlo methods (reinforcement learning) (also called Monte Carlo control): Learning methods that estimate values by averaging the actual total returns from complete runs, without needing a model of the world. They wait until a run ends to update, have no bias from bootstrapping, but their estimates can be noisy.
Monte Carlo methods (reinforcement learning): an example
Play 1,000 games from a position and average the final win or loss scores to estimate that position's value.
Related terms
More Reinforcement learning terms
- Monte Carlo tree search
- Multi-agent reinforcement learning
- Multi-armed bandit
- MuZero
- n-step return
- Nash equilibrium
- Natural policy gradient
- Noisy networks
Where you learn it
Monte Carlo methods (reinforcement learning) is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.