Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)

Huang Bojun

Room 301 - 303
[ Abstract ] [ Livestream: Visit Reinforcement Learning ]
Thu 21 Jul 11:50 a.m. — 11:55 a.m. PDT
[ Paper PDF

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 on the duality theory, and applies the algorithm to a state-of-the-art machine translation benchmark. The paper then turns to demonstrate a symmetry breaking phenomenon regarding the optimality of the Lagrangian saddle points, which justifies a largely overlooked direction in developing the Lagrangian method.

Chat is not available.