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Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes

Krishnendu Chatterjee ; Zuzana Křetínská ; Jan Křetínský.

We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives. There exist two different views: (i) the expectation semantics, where the goal is to optimize the expected mean-payoff objective, and (ii) the satisfaction semantics, where the goal is to maximize&nbsp;[&hellip;]

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Of Cores: A Partial-Exploration Framework for Markov Decision Processes

Jan Křetínský ; Tobias Meggendorfer.

We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties. The main idea is to identify a "core" of an MDP, i.e., a subsystem where we provably remain with high probability, and to avoid computation on the less&nbsp;[&hellip;]

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