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.
Sutton and Andrew G
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Sign-Separated Finite-Time Error Analysis of Q-Learning
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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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.