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On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes
Bruno Scherrer · Boris Lesner

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None
We consider infinite-horizon stationary $\gamma$-discounted Markov Decision Processes, for which it is known that there exists a stationary optimal policy. Using Value and Policy Iteration with some error $\epsilon$ at each iteration, it is well-known that one can compute stationary policies that are $\frac{2\gamma{(1-\gamma)^2}\epsilon$-optimal. After arguing that this guarantee is tight, we develop variations of Value and Policy Iteration for computing non-stationary policies that can be up to $\frac{2\gamma}{1-\gamma}\epsilon$-optimal, which constitutes a significant improvement in the usual situation when $\gamma$ is close to $1$. Surprisingly, this shows that the problem of ``computing near-optimal non-stationary policies'' is much simpler than that of ``computing near-optimal stationary policies''.

Author Information

Bruno Scherrer (INRIA)
Boris Lesner (INRIA)

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