Robust Partially Observable Markov Decision Processes
نویسندگان
چکیده
منابع مشابه
Partially observable Markov decision processes
For reinforcement learning in environments in which an agent has access to a reliable state signal, methods based on the Markov decision process (MDP) have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the MDP framework, partially observable Markov...
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. We present quantum observable Markov decision processes (QOMDPs), the quantum analogs of partially observable Marko...
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In the field of reinforcement learning (Sutton and Barto, 1998; Kaelbling et al., 1996), agents interact with an environment to learn how to act to maximize reward. Two different kinds of environment models dominate the literature—Markov Decision Processes (Puterman, 1994; Littman et al., 1995), or MDPs, and POMDPs, their Partially Observable counterpart (White, 1991; Kaelbling et al., 1998). B...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2018
ISSN: 1556-5068
DOI: 10.2139/ssrn.3195310