Machine learning model generation apparatus, machine learning model generation method, and non-transitory computer readable medium

A machine learning model generation method comprising:

• executing movement processing of moving a sample, among a plurality of samples included in a target sample group, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 (t is a natural number) being larger than a predetermined amount, from the target sample group to a source sample group;
• generating a plurality of weak learners by using at least observation data from time t to time T of at least one sample included in the target sample group after the movement processing and at least one sample included in the source sample group after the movement processing;
• and
• generating a t-th order machine learning model, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at time t of the at least one sample included in the target sample group after the movement processing, wherein
• the observation data include at least a state and an action of a sample at a specific time until time T, and
• the t-th order machine learning model outputs an action at time t by using at least a state at time t as an input.

A non-transitory computer readable medium storing a program for causing a computer to execute:

• movement processing of moving a sample, among a plurality of samples included in a target sample group, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 (t is a natural number) being larger than a predetermined amount, from the target sample group to a source sample group;
• processing of generating a plurality of weak learners by using at least observation data from time t to time T of at least one sample included in the target sample group after the movement processing and at least one sample included in the source sample group after the movement processing;
• and
• processing of generating a t-th order machine learning model, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at time t of the at least one sample included in the target sample group after the movement processing, wherein
• the observation data include at least a state and an action of a sample at a specific time until time T, and
• the t-th order machine learning model outputs an action at time t by using at least a state at time t as an input.