Mainstream RL's exploration algorithms trace to an independent 1933 bandit lineage, not to Fel'dbaum's dual control theory; his formalism maps cleanly only onto Bayesian reinforcement learning

feldbaumdual-control-theoryreinforcement-learningbayesian-reinforcement-learningmulti-armed-banditexploration-exploitationhistory-of-science

The vault's existing claim that Fel'dbaum's dual control theory is the origin of the exploration-exploitation tradeoff (claim-feldbaum-1960s-dual-control-formalized-exploration-exploitation-tradeoff) rests on Meijer and Rantzer's 2026 survey statement that dual control "propagated into a wide variety of subject areas in engineering, including adaptive control, reinforcement learning, and Bayesian optimization." The same survey's own structure resists reading that as one continuous lineage into mainstream RL. Its abstract frames the review as covering "four major research directions: Multi-armed bandits, self-tuning regulators, regret rate minimizing controllers, and minimax optimal dual controllers," and its account of the first traces to an entirely separate 1933 origin: "Already in 1933, Thompson (7) gave a mathematical formulation of the multi-armed bandit problem in the context of clinical trials." The survey's own multi-armed-bandit section — covering Thompson sampling, the Gittins index, Lai and Robbins' logarithmic regret bounds, and "optimism in the face of uncertainty" (the ancestor of UCB-style exploration) — names Fel'dbaum nowhere within it; he appears only in the historical preamble and once more, later, purely as a borrowed naming convention for an unrelated minimax reformulation. The survey frames the coexistence of the strands as parallel convergent discovery, not lineal descent: "The interplay between exploration and exploitation has been studied and rediscovered repeatedly in different scientific fields."

Where Fel'dbaum's specific formalism does map cleanly is onto a narrower, later subfield. Klenske and Hennig's 2016 JMLR paper states this precisely: "Feldbaum (1960–1961) coined the term dual control to describe the idea now also known as Bayesian reinforcement learning in the machine learning community" — the subfield that maintains and plans over explicit posterior beliefs, not the model-free, heuristic exploration methods (epsilon-greedy, tabular Q-learning, UCB bandits) that dominate the RL curriculum most practitioners encounter first, and that historically descend from the 1933–1985 statistics/operations-research lineage instead. The one-hop bridge — "Fel'dbaum formalized exploration-exploitation, which is now central to RL" — is true at the level of shared mathematical structure and shared vocabulary for the underlying problem, but elides that two historically independent lineages ran in parallel for decades and were stitched together only by later bridge work (Åström's 1965 POMDP framing; Klenske and Hennig in 2016), not by direct technical descent from Fel'dbaum's own equations into the bandit algorithms most reinforcement-learning systems actually run.