An autonomous agent maintains and updates an underlying model in a dynamic system. The autonomous agent receives minimum acceptable criteria from secondary users of the underlying model. The agent then compares current output samples of the model with the minimum acceptable criteria. If output samples do not meet the minimum acceptable criteria, then the agent formulates alternative model improvement actions and evaluates each alternative action by modeling rewards associated with it. The agent executes the alternative model improvement action having the highest reward.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An optimization agent system comprising: one or more processors; a communications interface connecting the one or more processors with a dynamic computer system, the dynamic computer system being described by a machine learning model; a communications interface connecting the one or more processors with a secondary user; at least one computer-readable storage device having stored thereon computer readable instructions for maintaining the dynamic system, wherein execution of the computer readable instructions by the one or more processors causes the processors to perform operations comprising: receiving, via the interface with the secondary user, minimum acceptable criteria on performance of the dynamic system; making a comparison of current output samples of the dynamic system to the minimum acceptable criteria, the comparison being made independently of the machine learning model; based on the comparison, formulating a plurality of alternative model improvement actions; evaluating each of the model improvement actions by modeling rewards associated with each model improvement action based on previous outcomes resulting from implementing previous model improvement actions; and altering the dynamic system to implement an optimal model improvement action having a greatest reward of the model improvement actions. 2. The optimization agent system of claim 1, wherein formulating the plurality of alternative model improvement actions includes formulating a retraining of the machine learning model. 3. The optimization agent system of claim 2, wherein formulating a retraining of the machine learning model further comprises changing parameters used by the model. 4. The optimization agent system of claim 1, wherein formulating the plurality of alternative model improvement actions includes formulating a partial update of the model. 5. The optimization agent system of claim 1, wherein formulating the plurality of alternative model improvement actions includes post processing an output of the model. 6. The optimization agent system of claim 1, wherein the current output samples are unlabeled. 7. The optimization agent system of claim 1, wherein the dynamic system is a software defined network. 8. The optimization agent system of claim 1, wherein modeling rewards associated with each model improvement action further comprises computing the rewards using the minimum acceptable criteria. 9. A computer-readable storage device having stored thereon computer readable instructions for maintaining a dynamic system described by a machine learning model, wherein execution of the computer readable instructions by a processor causes the processor to perform operations comprising: updating a reinforcement learning algorithm using outcomes resulting from implementing previous model improvement actions on the machine learning model; receiving minimum acceptable criteria on performance of the dynamic system; making a comparison of current output samples of the dynamic system to the minimum acceptable criteria, the comparison being made independently of the machine learning model; based on the comparison, formulating a plurality of alternative model improvement actions; evaluating each of the model improvement actions by modeling rewards associated with each model improvement action using the reinforcement learning algorithm; and altering the dynamic system to implement an optimal model improvement action having a greatest reward of the model improvement actions. 10. A method for maintaining a dynamic system, the dynamic system comprising a machine learning model, the method comprising: receiving minimum acceptable criteria on performance of the dynamic system; making a comparison of current output samples of the dynamic system to the minimum acceptable criteria, the comparison being made independently of the machine learning model; based on the comparison, formulating a plurality of alternative model improvement actions; evaluating each of the model improvement actions by modeling rewards associated with each model improvement action using a reinforcement learning algorithm operating on previous outcomes resulting from implementing previous model improvement actions; and altering the dynamic system to implement an optimal model improvement action having a greatest reward of the model improvement actions. 11. The method of claim 10, wherein formulating the plurality of alternative model improvement actions includes formulating a retraining of the machine learning model. 12. The method of claim 11, wherein formulating a retraining of the machine learning model further comprises changing parameters used by the model. 13. The method of claim 10, wherein formulating the plurality of alternative model improvement actions includes formulating a partial update of the model. 14. The method of claim 10, wherein formulating the plurality of alternative model improvement actions includes post processing an output of the model. 15. The method of claim 10, wherein the current output samples are unlabeled. 16. The method of claim 10, wherein the dynamic system is a software defined network. 17. The method of claim 10, wherein modeling rewards associated with each model improvement action further comprises computing the rewards using the minimum acceptable criteria. 18. The method of claim 10, wherein modeling rewards associated with each model improvement action is independent of the minimum acceptable criteria. 19. The method of claim 18, wherein modeling rewards associated with each model improvement action considers an amount of resources consumed. 20. The method of claim 18, wherein modeling rewards associated with each model improvement action considers abandoning a previously proven historical model.