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• W2951700521 abstract "This brief paper presents simple simulation-based algorithms for obtaining an approximately optimal policy in a given finite set in large finite constrained Markov decision processes. The algorithms are adapted from playing strategies for experts and bandits problem and their computational complexities are independent of state and action space sizes if the given policy set is relatively small. We establish convergence of their expected performances to the value of an optimal policy and convergence rates, and also almost-sure convergence to an optimal policy with an exponential rate for the algorithm adapted within the context of sleeping experts." @default.
• W2951700521 created "2019-06-27" @default.
• W2951700521 creator A5041693183 @default.
• W2951700521 date "2014-12-16" @default.
• W2951700521 modified "2023-09-27" @default.
• W2951700521 title "Sleeping Experts and Bandits Approach to Constrained Markov Decision Processes" @default.
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• W2951700521 hasPublicationYear "2014" @default.
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