Reducing computational costs to perform machine learning tasks
AssigneeInternational Business Machines Corporation
InventorsTetsuro Morimura, Michiko Okudo, Takayuki Katsuki
Life of this patent
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Abstract
A computer-implemented method for reducing computational costs for reducing computational costs to perform machine learning tasks includes generating one or more state partitioning candidates corresponding to a plurality of states associated with a partially observable Markov decision process (POMDP) model, determining that a given state partitioning candidate of the one or more state partitioning candidates satisfies a merge condition based on a state transition matrix for the given state partitioning candidate, and performing a machine learning task based on the POMDP model with merged states using the given state partitioning candidate.
Claims20
A computer-implemented method for reducing computational costs to perform machine learning tasks, comprising:
- generating, by at least one processor device operatively coupled to a memory, one or more state partitioning candidates corresponding to a plurality of states associated with the a partially observable Markov decision process (POMDP) model;
- determining, by the at least one processor device, that a given state partitioning candidate of the one or more state partitioning candidates satisfies a merge condition based on a state transition matrix for the given state partitioning candidate;
- and
- performing, by the at least one processor device, a machine learning task based on the POMDP model with merged states using the given state partitioning candidate.
A system for reducing computational costs for machine learning tasks using partially observable Markov decision processes (POMDP) models, comprising:
- a memory device for storing program instructions;
- and
- at least one processor device operatively coupled to the memory device and configured to execute program code stored on the memory device to:
- generate one or more state partitioning candidates corresponding to a plurality of states associated with a partially observable Markov decision process (POMDP) model;
- determine that a given state partitioning candidate of the one or more state partitioning candidates satisfies a merge condition based on a state transition matrix for the given state partitioning candidate;
- and
- perform a machine learning task based on the POMDP model with merged states using the given state partitioning candidate.
A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method for reducing computational costs to perform machine learning tasks, the method performed by the computer comprising:
- generating one or more state partitioning candidates corresponding to a plurality of states associated with a partially observable Markov decision process (POMDP) model;
- determining that a given state partitioning candidate of the one or more state partitioning candidates satisfies a merge condition based on a state transition matrix for the given state partitioning candidate;
- and
- performing a machine learning task based on the POMDP model with merged states using the given state partitioning candidate.
Dependencies are inferred from the claim text.