RL experiments are fragile? New paper guides rigorous design and comparison
burkov · x · 2026-08-27
Reinforcement learning experiments are unusually fragile; two implementations of the same idea can look different due to random initialization, data, hyperparameters, or minor environment details. Patterson et al. provide a practical guide on designing experiments to separate real algorithmic differences from noise, covering how to measure variance across runs, distinguish uncertainty, and handle hyperparameters fairly.
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