Same Algorithm, Different Outcome: What 33,000 Actor-Critic Runs Reveal
TL;DR for operators Two reinforcement-learning systems can use the same named algorithm, train on the same task, and still produce materially different outcomes across random seeds. Shah, Zhu, White, and White investigate why by decomposing actor-critic systems into their lower-level choices rather than treating PPO, SAC, DDPG, or MPO as indivisible packages.1 ...