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.