Monte Carlo On-Policy for the Maze Problem

Author(s): Rem E

Originally published on Towards AI.

Tutorial 8: Implementing the On-Policy MC Method for Our Maze Problem

Let’s take another step forward in solving RL problems by implementing our second method: Monte Carlo!
This tutorial builds directly on Tutorial 7, so check that out first if you haven’t already!
If you’re not familiar with the Monte Carlo On-Policy algorithm, you can catch up Back Again to Monte Carlo. We’re diving back into Monte Carlo because now it’s time to implement it!

The article discusses the implementation of Monte Carlo On-Policy methods for reinforcement learning, detailing the setup required and differences from dynamic programming. It provides specific code examples, challenges readers to apply what they’ve learned, and warns of potential pitfalls encountered during development. The conclusion summarizes the importance of understanding these Monte Carlo methods in creating effective agents that learn optimal policies in complex environments.

Read the full blog for free on Medium.

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Published via Towards AI