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Beyond Markov Decision Process with Scalar Markovian Rewards
Proceedings of the International Symposium on Combinatorial Search
Real-world decision problems often involve multiple competing objectives or a complex reward structure that violate Markov assumption. However, the existing research on sequential decision making under uncertainty primarily focused on Markov Decision Processes (MDPs) with scalar Markovian reward signals. My thesis considers settings where scalar Markovian rewards are not sufficient to produce desired behaviors. The first part of my thesis develops algorithms to optimize lexicographicallydoi:10.1609/socs.v15i1.21805 fatcat:i236hwkmsva2dhhksbjbmpo6mu