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.
Computationally efficient approximations of the joint spectral radius.SIAM Journal on Matrix Analysis and Applications, 27(1):256–272
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Q-learning error is recast as a switched linear recursion whose exponential rate is exactly the joint spectral radius of a direct switching family, yielding finite-time bounds via a product-defined Lyapunov function.
citing papers explorer
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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.
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Lyapunov-Certified Direct Switching Theory for Q-Learning
Q-learning error is recast as a switched linear recursion whose exponential rate is exactly the joint spectral radius of a direct switching family, yielding finite-time bounds via a product-defined Lyapunov function.