Summary  
This chapter explains the policy iteration algorithm, which alternates between fully evaluating a given policy’s value function and greedily improving the policy based on that value until convergence.

General domain of usage  
Reinforcement learning

The idea behind **policy iteration** is simple:
1. Take some initial $$\pi$$ and $$v$$;
2. Use policy evaluation to update $$v$$ until it's consistent with $$\pi$$;
3. Use policy improvement to update $$\pi$$ until it's greedy with respect to $$v$$;
4. Repeat steps 2-3 until convergence.

In this method, there are **no partial updates**:
- During **policy evaluation**, values are updated for each state, until they are consistent with current policy;
- During **policy improvement**, policy is made greedy with respect to value function.

Based on the pseudocode, what condition causes the outer loop of policy iteration to stop?

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Policy Iteration

Pseudocode

Awesome!

Policy Iteration

Pseudocode