Run systematic RL debugging using the 80/20 rule - check environment and reward BEFORE changing algorithms
/plugin marketplace add tachyon-beep/skillpacks/plugin install yzmir-deep-rl@foundryside-marketplace[training_script.py or directory]# Diagnose Command Apply the 80/20 rule for RL debugging: 80% of failures are environment/reward issues, not algorithm problems. ## Core Principle **DO NOT suggest algorithm changes until environment and reward are verified.** ## Diagnostic Process ### Phase 1: Environment Sanity (Check First - Most Common Issues) 1. **Observation Space** - Does the agent observe everything it needs to solve the task? - Are observations normalized (roughly [-1, 1] or [0, 1])? - Any information leakage (observing future states)? 2. **Action Space** - Discrete vs continuous - is algorithm...