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 delves into the concept of Off-Policy control in Monte Carlo reinforcement learning, contrasting it with On-Policy methods. It highlights the roles of target and behavior policies, the significance of importance sampling for correcting distribution mismatches, and the advantages of flexibility in exploration without affecting performance. Through detailed mathematical review and algorithmic implementation, including an incremental approach, the article emphasizes practical applications and key takeaways from Off-Policy methods, ultimately guiding readers to further explore their potential in solving reinforcement learning challenges.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI