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Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)
International Conference on Machine Learning
This paper discusses a new approach to the fundamental problem of learning optimal Q-functions. In this approach, optimal Q-functions are formulated as saddle points of a nonlinear Lagrangian function derived from the classic Bellman optimality equation. The paper shows that the Lagrangian enjoys strong duality, in spite of its nonlinearity, which paves the way to a general Lagrangian method to Q-function learning. As a demonstration, the paper develops an imitation learning algorithm based ondblp:conf/icml/Huang22 fatcat:7odxs4rejncprihfzt6xqeww74