Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling

11 Jun 2013L. A. PrashanthNathaniel KordaRémi Munos

We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our proposed scheme is equivalent to running regular temporal difference learning with linear function approximation, albeit with samples picked uniformly from a given dataset... (read more)

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