MiS Preprint Repository
We have decided to discontinue the publication of preprints on our preprint server end of 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.
Task-agnostic constraining in average reward POMDPs
Guido Montúfar, Johannes Rauh and Nihat AyAbstract
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
Task-agnostic constraining in average reward POMDPs
In: Task-agnostic reinforcement learning : workshop at ICLR, 06 May 2019, New Orleans[S. L.] : ICLR, 2019.