This isn’t just another summary of RL mathematics, algorithm taxonomy, or package tutorial — there are already thousands of those. Instead, I offe

Learning RL is Challenging. Here’s a Guide for Smooth Sailing

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2024-09-25 15:00:09

This isn’t just another summary of RL mathematics, algorithm taxonomy, or package tutorial — there are already thousands of those.

Instead, I offer newcomers a strategic path into reinforcement learning. This approach aims to develop a 360-degree understanding: grasping RL’s core characteristics to identify suitable problems, familiarizing yourself with key elements to view challenges through an RL lens, and equipping yourself a toolbox of modern Python libraries for direct problem-solving.

In the following short prelude sections, I’ll explain why learning RL is especially challenging (compared to other STEM topics) and how I believe we can make it easier. Feel free to skip to the main path if you’re eager to get started.

Armed with the principles from the two preludes above, you’re already well-equipped to design a learning strategy for yourself. Nevertheless, allow me to showcase mine, which incorporates these insights and my experience.

All above resources are already covered in my last post <Denoised RL Starter Pack: a Curated Shortlist of Reinforcement Learning Resources>. There’re direct links and explanations on why they qualify as best resources.