Deflated Q-value iteration admits a projected switching-system model whose joint spectral radius can be strictly smaller than the discount factor, yielding a sharper convergence characterization while leaving the greedy policy sequence unchanged.
Dynamic programming and optimal con trol 4th edition, volume ii
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
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.
citing papers explorer
-
Switching-Geometry Analysis of Deflated Q-Value Iteration
Deflated Q-value iteration admits a projected switching-system model whose joint spectral radius can be strictly smaller than the discount factor, yielding a sharper convergence characterization while leaving the greedy policy sequence unchanged.
-
A Switching System Theory of Q-Learning with Linear Function Approximation
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.