rl-debugging

What problem does it solve?

This Skill provides a systematic framework to diagnose and fix RL training failures, guiding engineers through a prioritized set of checks and diagnostic trees to identify root causes.

Core Features & Use Cases

• Systematic debugging framework for RL: diagnosis trees, step-by-step checks, and practical guidance.
• Comprehensive symptom-based paths: "Agent Won't Learn", "Training Unstable", "Suboptimal Policy".
• Practical logging and monitoring guidance to surface actionable signals.
• Useful for rapid triage of ML training issues and improving reproducibility.

Quick Start

Invoke this skill to guide systematic diagnosis when an RL agent exhibits learning failures, instability, or suboptimal performance. Start by checking reward scale and environment sanity, then follow the diagnosis trees to identify root causes, compare against a common bugs catalog, and apply recommended fixes.