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We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint
9/2021

Task-agnostic constraining in average reward POMDPs

Guido Montúfar, Johannes Rauh and Nihat Ay

Abstract

We study the shape of the average reward as a function over the memoryless stochastic policies in infinite-horizon partially observed Markov decision processes. We show that for any given instantaneous reward function on state-action pairs, there is an optimal policy that satisfies a series of constraints expressed solely in terms of the observation model. Our analysis extends and improves previous descriptions for discounted rewards or which covered only special cases.

Received:
09.03.21
Published:
09.03.21
Keywords:
partial observability, Markov decision process, stochastic policy, memoryless policy, optimal planning

Related publications

inBook
2019 Repository Open Access
Guido Montúfar, Johannes Rauh and Nihat Ay

Task-agnostic constraining in average reward POMDPs

In: Task-agnostic reinforcement learning : workshop at ICLR, 06 May 2019, New Orleans
[S. L.] : ICLR, 2019.