Reinforcement learning’s most popular abandoned reading list

What it does — This is a classic “awesome” list: a single markdown page indexing reinforcement-learning resources. It catalogs lectures, textbooks, surveys, foundational papers, application domains, and open-source code libraries in one sprawling outline. Think of it as a comprehensive reading list and toolbox directory for the field.

The interesting bit — The list is essentially an archaeological dig site. It stretches from Marvin Minsky’s 1961 paper on the “credit assignment problem” to DeepMind’s 2021 UCL lectures, capturing what the field considered canonical before the maintainers abandoned it. The explicit “BROKEN LINK” warning next to Sutton’s MATLAB code is a rare moment of honesty in curation.

Key highlights

• Heavy academic focus: links to canonical courses (David Silver at UCL, Berkeley’s Deep RL Bootcamp, Stanford CS229) and seminal texts like Sutton & Barto.
• Code coverage spans eras, from legacy Java frameworks (Burlap, TeachingBox) and MATLAB GUIs to newer PyTorch and JAX implementations.
• Foundational papers section reaches back to 1961, tracing the intellectual lineage of temporal-difference learning and dynamic programming.
• Explicitly disclaims maintenance; the top of the README states “This page is no longer maintained.”
• 9,834 stars suggest it was once the default jumping-off point for RL students.

Caveats

• Stale by design: without active maintenance, links rot and recent developments are absent.
• Several entries are already marked broken, and the list’s structure predates many modern libraries.
• The scope is so broad that discoverability suffers; you need to know what you’re looking for.

Verdict — Browse it if you need a historical map of classic RL literature and legacy toolkits. Avoid it as a primary source; the curators have left and the list is frozen in time.