Error Propagation for Approximate Policy and Value Iteration

NeurIPS 2010 Amir-Massoud FarahmandCsaba SzepesváriRémi Munos

We address the question of how the approximation error/Bellman residual at each iteration of the Approximate Policy/Value Iteration algorithms influences the quality of the resulted policy. We quantify the performance loss as the Lp norm of the approximation error/Bellman residual at each iteration... (read more)

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