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When humans are given a policy to execute, there can be policy execution errors and deviations in policy if there is uncertainty in identifying a state.
Cost-sensitive robot learning
Tan, M. 1991 · 1991
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Learning to perceive and act by trial and error
Whitehead, S. D.; and Ballard, D. H. 1991 · 1991
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A survey of applications of Markov decision processes
White, D. J. 1993 · 1993
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Memoryless policies: Theoretical limitations and practical results
Littman, M. L. 1994 · 1994
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Reinforcement learning of non-Markov decision processes
Whitehead, S. D.; and Lin, L.-J. 1995 · 1995
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Branch-and-bound applications in combinatorial data analysis , volume 2
Brusco, M. J.; Stahl, S.; et al. 2005 · 2005
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Markov decision processes with their applications , volume 14
Hu, Q.; and Yue, W. 2007 · 2007
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Ibe, O. 2013 · 2013
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Solving POMDPs by searching the space of finite policies
Meuleau, N.; Kim, K.-E.; Kaelbling, L. P.; and Cassandra, A. R. 2013 · 2013
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History-based controller design and optimization for partially observable MDPs
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Markov decision processes in practice , volume 248
Boucherie, R. J.; and Van Dijk, N. M. 2017 · 2017
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Ramakrishnan, R.; Kamar, E.; Dey, D.; Horvitz, E.; and Shah, J. 2020 · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P.; Gommers, R.; Oliphant, T. E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; van der Walt, S. J.; Brett, M.; Wilson, J.; Millman, K. J.; Mayorov, N.; Nelson, A. R. J.; Jones, E.; Kern, R.; Larson, E.; Carey, C. J.; Polat, İ.; Feng, Y.; Moore, E. W.; VanderPlas, J.; Laxalde, D.; Perktold, J.; Cimrman, R.; Henriksen, I.; Quintero, E. A.; Harris, C. R.; Archibald, A. M.; Ribeiro, A. H.; Pedregosa, F.; van Mulbregt, P.; and SciPy 1.0 Contributors. 2020 · 2020
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A Bayesian Approach to Identifying Representational Errors
Ramakrishnan, R.; Unhelkar, V.; Kamar, E.; and Shah, J. 2021 · 2021
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Artificial Intelligence: A Modern Approach, Global Edition 4th
Russell, S.; and Norvig, P. 2021 · 2021
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