Monte Carlo Off-Policy Explained

Author(s): Rem E

Originally published on Towards AI.

Learning the Second Control Method in Monte Carlo Reinforcement Learning

Previously, we explored the On-Policy control method in Monte Carlo, where we evaluate and improve the same policy using the ε-greedy strategy to handle exploration (see Back Again to Monte Carlo). This time, we’ll dive into another method: Off-Policy, and see how it reshapes the way we solve RL problems!

The article explains the concept of Off-Policy control methods in Monte Carlo Reinforcement Learning, illustrating the differences between target and behavior policies, their respective roles in generating episodes, and the benefits of using Off-Policy methods, particularly through the importance sampling technique. It elaborates on how Off-Policy strategies allow for increased exploration without compromising the performance of the learning agent, and provides a breakdown of the mathematical foundations and algorithms involved in implementing Off-Policy control.

Read the full blog for free on Medium.

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Published via Towards AI