Value Function Discovery in Markov Decision Processes With Evolutionary Algorithms
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- @Article{Onderwater:2016::ieeeTSMC,
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author = "Martijn Onderwater and Sandjai Bhulai and
Rob {van der Mei}",
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journal = "IEEE Transactions on Systems, Man, and Cybernetics:
Systems",
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title = "Value Function Discovery in Markov Decision Processes
With Evolutionary Algorithms",
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year = "2016",
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volume = "46",
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number = "9",
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pages = "1190--1201",
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abstract = "In this paper, we introduce a novel method for the
discovery of value functions for Markov decision
processes (MDPs). This method, which we call value
function discovery (VFD), is based on ideas from the
evolutionary algorithm field. VFDs key feature is that
it discovers descriptions of value functions that are
algebraic in nature. This feature is unique, because
the descriptions include the model parameters of the
MDP. The algebraic expression of the value function
discovered by VFD can be used in several scenarios,
e.g., conversion to a policy (with one-step policy
improvement) or control of systems with time-varying
parameters. The work in this paper is a first step
toward exploring potential usage scenarios of
discovered value functions. We give a detailed
description of VFD and illustrate its application on an
example MDP. For this MDP, we let VFD discover an
algebraic description of a value function that closely
resembles the optimal value function. The discovered
value function is then used to obtain a policy, which
we compare numerically to the optimal policy of the
MDP. The resulting policy shows near-optimal
performance on a wide range of model parameters.
Finally, we identify and discuss future application
scenarios of discovered value functions.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/TSMC.2015.2475716",
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ISSN = "2168-2216",
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month = sep,
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notes = "Also known as \cite{7274364}",
- }
Genetic Programming entries for
Martijn Onderwater
Sandjai Bhulai
Rob van der Mei
Citations