AI engineering glossary · Reinforcement learning
What is policy iteration?
Policy iteration: A planning method that alternates between computing the values of the current decision rule and improving the rule greedily with respect to those values. It stops when the rule no longer changes, and in a finite model it reaches an optimal rule.
Policy iteration: an example
On a small grid world, 3 or 4 rounds of evaluate-then-improve are typically enough to settle on the best route.
Related terms
More Reinforcement learning terms
- Potential-based reward shaping
- Prioritised experience replay
- Q-learning
- Q-table
- Rainbow
- Regret
- Reinforcement learning
- Return
Where you learn it
Policy iteration is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.