Deep reinforcement learning for air handling control

Abstract

An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling actuators in response to receiving the control signal. In one aspect of the embodiment, the control signal is a command signal for the air handling actuators.