Debugging Reinforcement Learning Systems

Debugging reinforcement learning implementations, without the agonizing pain.

Debugging Reinforcement Learning Systems andy jones Debugging RL, Without the Agonizing Pain Debugging reinforcement learning systems combines the pain of debugging distributed systems with the pain of debugging numerical optimizers. Which is to say, it sucks . If this is your first time, you might have a few hundred lines of code that you think are correct in an hour, and a system that's actually correct two months later. Here's the head of Tesla AI having just that experience . This is a collection of debugging advice that has served me well over the past few years. It was formed both from m

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related reading

• A Recipe for Training Neural Networkskarpathy.github.io
• Reward Hacking in Reinforcement Learning | Lil'Loglilianweng.github.io
• Deep Q-Networks Explained — LessWronglesswrong.com
• The 37 Implementation Details of Proximal Policy Optimization · The ICLR Blog Trackiclr-blog-track.github.io
• Key Papers in Deep RL - Spinning Up documentationspinningup.openai.com
• GRPO++: Tricks for Making RL Actually Workcameronrwolfe.substack.com
• Part 1: Key Concepts in RL - Spinning Up documentationspinningup.openai.com
• Deep Reinforcement Learning: Pong from Pixelskarpathy.github.io
• Part 2: Kinds of RL Algorithms - Spinning Up documentationspinningup.openai.com
• Reinforcement Learning in Newcomblike Problemsproceedings.neurips.cc
• State of RL for reasoning LLMs | A. Weersaweers.de
• Keep the Tokens Flowing: Lessons from 16 Open-Source RL Librarieshuggingface.co