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This paper compiles several aspects of the dynamics of stochastic approximation algorithms with Markov iterate-dependent noise when the iterates are not known to be stable beforehand.
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H.F.Chen, “Stochastic approximation and its applications,” SIAM Journal on Control and Optimization
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G.Dalal, B.Szorenyi, G.Thoppe, and S.Mannor, “Finite sample analysis of two-timescale stochastic approximation with applications to reinforcement learning,” Conference on Learning Theory (COLT)
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A.Ramaswamy and S.Bhatnagar, “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning,” IEEE Transactions on Automatic Control
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