How To Code Policy Iteration | Free Reinforcement Learning Course Module 5b | Summary and Q&A

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April 17, 2019
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Machine Learning with Phil
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How To Code Policy Iteration | Free Reinforcement Learning Course Module 5b

TL;DR

Policy iteration is a slow method for convergence compared to value iteration in reinforcement learning.

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Questions & Answers

Q: Is policy iteration faster than value iteration in reinforcement learning?

No, value iteration is faster to converge compared to policy iteration. Policy iteration is relatively slower.

Q: What is the purpose of policy improvement in reinforcement learning?

The purpose of policy improvement is to transform the current policy into a better policy by updating the actions based on the estimated value function.

Q: How is the stability of the policy determined in policy iteration?

The stability of the policy is determined by checking if the old action is different from the new action. If they are different, the policy is considered unstable.

Q: How many sweeps of the state space are required in policy iteration?

In the given example, 316,400 sweeps of the state space were required for policy iteration, along with additional sweeps for policy evaluation.

Summary & Key Takeaways

  • Policy iteration is composed of policy evaluation and policy improvement.

  • The policy evaluation has already been implemented in the previous module.

  • The current module focuses on implementing the policy improvement step.

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