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Paternain et al., 2018 - Google Patents

Learning policies for markov decision processes in continuous spaces

Paternain et al., 2018

Document ID
10833729593636862068
Author
Paternain S
Bazerque J
Small A
Ribeiro A
Publication year
Publication venue
2018 IEEE Conference on Decision and Control (CDC)

External Links

Snippet

In this work we consider the problem of policy optimization in the context of reinforcement learning. In order to avoid discretization, we select the optimal policy to be a continuous function belonging to a reproducing Kernel Hilbert Space (RKHS) which maximizes an …
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Classifications

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    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/62—Methods or arrangements for recognition using electronic means
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    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
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