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Optimistic Value Iteration
[chapter]
2020
Lecture Notes in Computer Science
Markov decision processes are widely used for planning and verification in settings that combine controllable or adversarial choices with probabilistic behaviour. The standard analysis algorithm, value iteration, only provides lower bounds on infinite-horizon probabilities and rewards. Two "sound" variations, which also deliver an upper bound, have recently appeared. In this paper, we present a new sound approach that leverages value iteration's ability to usually deliver good lower bounds: we
doi:10.1007/978-3-030-53291-8_26
fatcat:fjoe3ibrgfdpxboytbv4cn5sse