An apparatus for executing learning of a control model for cell culture in a bioreactor which operates according to an operation content, wherein the operation content includes active variables which include at least one of a set point of a process value, a manipulate variable, an equipment requirement for manufacturing or a processing order, the apparatus comprising: a control model for controlling the bioreactor which is configured to output an operation content that increases a reward value in response to input of a state parameter set, wherein the control model is a machine learning algorithm, wherein the state parameter set includes at least one state parameter indicating a state of at least one of the bioreactor or the cell, a setting unit configured to set the operation content provided by the control model for the bioreactor , a first acquisition unit configured to acquire, as the state parameter set, a posterior state parameter set including at least one posterior state parameter indicating a state of at least one of the bioreactor or the cell after the operation content is set, a calculation unit configured to calculate a reward value from at least one state parameter by a predetermined reward function, a learning processing unit configured to receive the calculated reward value, and execute a learning process of the control model of the bioreactor , by using learning data including the operation content and the posterior state parameter set. wherein the operation content includes active variables which include at least one of a set point of a process value, a manipulate variable, an equipment requirement for manufacturing or a processing order, the apparatus comprising: a control model for controlling the bioreactor which is configured to output an operation content that increases a reward value in response to input of a state parameter set, wherein the control model is a machine learning algorithm, wherein the state parameter set includes at least one state parameter indicating a state of at least one of the bioreactor or the cell, a setting unit configured to set the operation content provided by the control model for the bioreactor , a first acquisition unit configured to acquire, as the state parameter set, a posterior state parameter set including at least one posterior state parameter indicating a state of at least one of the bioreactor or the cell after the operation content is set, a calculation unit configured to calculate a reward value from at least one state parameter by a predetermined reward function, a learning processing unit configured to receive the calculated reward value, and execute a learning process of the control model of the bioreactor , by using learning data including the operation content and the posterior state parameter set. a control model for controlling the bioreactor which is configured to output an operation content that increases a reward value in response to input of a state parameter set, wherein the control model is a machine learning algorithm, wherein the state parameter set includes at least one state parameter indicating a state of at least one of the bioreactor or the cell, a setting unit configured to set the operation content provided by the control model for the bioreactor , a first acquisition unit configured to acquire, as the state parameter set, a posterior state parameter set including at least one posterior state parameter indicating a state of at least one of the bioreactor or the cell after the operation content is set, a calculation unit configured to calculate a reward value from at least one state parameter by a predetermined reward function, a learning processing unit configured to receive the calculated reward value, and execute a learning process of the control model of the bioreactor , by using learning data including the operation content and the posterior state parameter set. <EOS>