Hahn, Ernst Moritz, Perez, Mateo, Schewe, Sven 
ORCID: 0000-0002-9093-9518, Somenzi, Fabio, Trivedi, Ashutosh and Wojtczak, Dominik ORCID: 0000-0001-5560-0546
(2024)
Omega-Regular Decision Processes.
In: AAAI 2024.
Abstract
<jats:p>Regular decision processes (RDPs) are a subclass of non-Markovian decision processes where the transition and reward functions are guarded by some regular property of the past (a lookback). While RDPs enable intuitive and succinct representation of non-Markovian decision processes, their expressive power coincides with finite-state Markov decision processes (MDPs). We introduce omega-regular decision processes (ODPs) where the non-Markovian aspect of the transition and reward functions are extended to an omega-regular lookahead over the system evolution. Semantically, these lookaheads can be considered as promises made by the decision maker or the learning agent about her future behavior. In particular, we assume that, if the promised lookaheads are not met, then the payoff to the decision maker is falsum (least desirable payoff), overriding any rewards collected by the decision maker. We enable optimization and learning for ODPs under the discounted-reward objective by reducing them to lexicographic optimization and learning over finite MDPs. We present experimental results demonstrating the effectiveness of the proposed reduction.</jats:p>
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Basic Behavioral and Social Science, Behavioral and Social Science |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 20 Dec 2023 15:37 |
| Last Modified: | 14 Apr 2024 02:53 |
| DOI: | 10.1609/aaai.v38i19.30105 |
| Open Access URL: | https://arxiv.org/pdf/2312.08602.pdf |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3177566 |
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