How to Code Policy Evaluation  Free Reinforcement Learning Course Module 5a  Summary and Q&A
TL;DR
This video is part of a reinforcement learning course and covers the policy evaluation algorithm using a grid world example.
Questions & Answers
Q: What is policy evaluation in reinforcement learning?
Policy evaluation is the process of estimating the value function for a given policy by iteratively calculating the expected rewards for each state.
Q: Why is the initial estimate for the value function set to optimistic values?
Optimistic initial values encourage exploration in the beginning, even if the policy is purely greedy. This helps the agent to discover potentially better actions and improve the policy.
Q: What is the purpose of the equiprobable random strategy for the policy?
The equiprobable random strategy assigns equal probabilities to each possible action in each state. It is a reasonable starting point and allows for exploration during policy iteration.
Q: How is convergence checked in policy evaluation?
Convergence is checked by calculating the difference (Delta) between the old and new value functions. If Delta is smaller than a predefined threshold (theta), the algorithm is considered to have converged.
Summary & Key Takeaways

The video introduces the topic of policy evaluation in reinforcement learning and explains that the algorithms will be implemented in a grid world environment.

The code provided in the video implements the necessary functions to initialize state transition probabilities and print the value function and policy.

The video also discusses the concepts of convergence criteria, initial estimate for the value function, and the equiprobable random strategy for the policy.