Deep RL's missing table of contents

What it does This is a classic awesome-list: a curated index of deep reinforcement learning resources. It collects libraries, benchmark suites, environments, competitions, books, and tutorials into a single markdown document. Think of it as a card catalog for a field that produces new frameworks faster than most people can bookmark them.

The interesting bit The list is pragmatically organized by function rather than hype—libraries, benchmarks, environments—so you can compare Ray RLLib against Stable Baselines or find a domain-specific simulator without opening twenty tabs.

Key highlights

• Covers the full stack: algorithm libraries (e.g., RLtools, Tianshou), benchmark results, and environments ranging from MuJoCo to Microsoft AirSim and Animal-AI Olympics.
• Includes niche and domain-specific simulators like BTGym for backtesting and gym-idsgame for cybersecurity.
• Maintained as plain markdown, making it forkable and diff-friendly for contributors.
• Sections on competitions, timelines, books, and blogs provide context beyond raw code.

Caveats

• The README is a flat link list with minimal annotation; you still need to click through to evaluate whether a library is maintained or experimental.
• Some entries lack descriptions beyond their one-line GitHub taglines, so the curation is broad rather than deeply opinionated.

Verdict Worth bookmarking if you are surveying the deep RL landscape or choosing a stack for a new project. Skip it if you already know exactly which framework you need and just want the docs.