Sign-separated analysis decomposes Q-learning errors into negative parts dominated by an optimal-policy LTI system and positive parts controlled by a switching system, yielding finite-time bounds for deterministic and stochastic cases.
Springer Science & Business Media
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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.
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.
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Sign-separated analysis decomposes Q-learning errors into negative parts dominated by an optimal-policy LTI system and positive parts controlled by a switching system, yielding finite-time bounds for deterministic and stochastic cases.
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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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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.