Periodic and soft target updates guarantee convergence in linear Q-learning to the exact projected Q-Bellman solution under spectral and step-size conditions via joint spectral radius analysis of switched linear systems.
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Derives an exact linear switched model for the mean dynamics of Q-learning with linear function approximation and relates convergence to joint spectral radius stability of the switched system, extending the view to stochastic and regularized cases.
Introduces and analyzes the λ-target update for linear Q-learning via geometric averaging of periodic target maps, studied with a switching-system model in the deterministic case.
Derives optimal strategies for a partially observed Stackelberg SDE game with asymmetric information and extends deterministic multi-agent formation control to the stochastic case.
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Target Updates May Stabilize Linear Q-Learning: Periodic and Soft Dynamics
Periodic and soft target updates guarantee convergence in linear Q-learning to the exact projected Q-Bellman solution under spectral and step-size conditions via joint spectral radius analysis of switched linear systems.