Monte Carlo Off-Policy for the Maze Problem

Author(s): Rem E

Originally published on Towards AI.

Tutorial 8.2: Implementing the Off-Policy MC Method for Our Maze Problem

We learned all about On-Policy Monte Carlo. Now let’s bring Off-Policy to life!
This tutorial builds directly on Tutorial 8.1, so check that out first if you haven’t already!
If you’re not familiar with the Monte Carlo Off-Policy algorithm, take a look at Monte Carlo Off-Policy Explained. Now it’s time to implement it!

This tutorial emphasizes the implementation of off-policy Monte Carlo methods, detailing the theoretical and practical components necessary for effective execution. Key concepts include the separation of target and behavior policies and the use of an incremental approach to update value estimates. The importance of policy improvement and convergence is discussed, alongside practical coding applications and optimizations to enhance performance in reinforcement learning environments.

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Published via Towards AI