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Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraints

Authors Jan Kretínský , Guillermo A. Pérez , Jean-François Raskin

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Author Details

Jan Kretínský
  • Technische Universität München, Munich, Germany
Guillermo A. Pérez
  • Université libre de Bruxelles, Brussels, Belgium
Jean-François Raskin
  • Université libre de Bruxelles, Brussels, Belgium

Funding

This research was funded in part by the Czech Science Foundation grant No. 18-11193S, the German Research Foundation (DFG) project 383882557 "Statistical Unbounded Verification", the ERC Starting grant 279499 "inVEST", the ARC (Fédération Wallonie-Bruxelles) project "Non-Zero Sum Game Graphs: Applications to Reactive Synthesis and Beyond", and the EOS (FNRS-FWO) project 30992574 "Verifying Learning Artificial Intelligence Systems".
  • Pérez, Guillermo A.: G. A. Pérez has been supported by an F.R.S.-FNRS Aspirant fellowship.
  • Raskin, Jean-François: J.-F. Raskin is Professeur Francqui de Recherche funded by the Francqui foundation.

Cite As Get BibTex

Jan Kretínský, Guillermo A. Pérez, and Jean-François Raskin. Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraints. In 29th International Conference on Concurrency Theory (CONCUR 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 118, pp. 8:1-8:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.CONCUR.2018.8

Abstract

We formalize the problem of maximizing the mean-payoff value with high probability while satisfying a parity objective in a Markov decision process (MDP) with unknown probabilistic transition function and unknown reward function. Assuming the support of the unknown transition function and a lower bound on the minimal transition probability are known in advance, we show that in MDPs consisting of a single end component, two combinations of guarantees on the parity and mean-payoff objectives can be achieved depending on how much memory one is willing to use. (i) For all epsilon and gamma we can construct an online-learning finite-memory strategy that almost-surely satisfies the parity objective and which achieves an epsilon-optimal mean payoff with probability at least 1 - gamma. (ii) Alternatively, for all epsilon and gamma there exists an online-learning infinite-memory strategy that satisfies the parity objective surely and which achieves an epsilon-optimal mean payoff with probability at least 1 - gamma. We extend the above results to MDPs consisting of more than one end component in a natural way. Finally, we show that the aforementioned guarantees are tight, i.e. there are MDPs for which stronger combinations of the guarantees cannot be ensured.

Subject Classification

ACM Subject Classification
  • Theory of computation → Logic and verification
  • Theory of computation → Reinforcement learning
Keywords
  • Markov decision processes
  • Reinforcement learning
  • Beyond worst case

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