A PID controller system comprising: a proportional, integral and derivative, PID, controller configured to output a control action to a controlled process module (62, 81), the control action is based on an output provided to the PID controller and is configured to affect a value of a controlled variable; a machine learning module having a reinforcement module and a process state augmentation and differentiation module, the reinforcement module is configured to provide the output to the PID controller via an output connection to the PID controller; and the controlled process module (62, 81) is configured to receive, as an input, the control action outputted from the PID controller, the controlled process module is configured to output one or more process states as well as a value for the controlled variable to a differentiator that also takes in a set-point (66, 82) for the controlled variable and the differentiator outputs a control error to the PID controller, wherein the control error represents a difference between the set-point (66, 82) and the value of the controlled variable; wherein: the controlled variable, the set-point (66, 82) for the controlled variable, and the one or more process states are input to the process state augmentation and differentiation module of the machine learning module; the process state augmentation and differentiation module arranges time differences of the one or more process states into a vector and augments the vector with the corresponding control error to produce a time-differentiated and augmented variable; and the machine learning module calculates an instantaneous loss or reward which is an increasing or decreasing, respectively, function of the control error; and the reinforcement module is configured to receive the instantaneous loss or reward, a time difference of control actions, and the time-differentiated and augmented variable, and determines the output provided to the PID controller based on the instantaneous loss or reward, the time difference of control actions, and the time-differentiated and augmented variable. a proportional, integral and derivative, PID, controller configured to output a control action to a controlled process module (62, 81), the control action is based on an output provided to the PID controller and is configured to affect a value of a controlled variable; a machine learning module having a reinforcement module and a process state augmentation and differentiation module, the reinforcement module is configured to provide the output to the PID controller via an output connection to the PID controller; and the controlled process module (62, 81) is configured to receive, as an input, the control action outputted from the PID controller, the controlled process module is configured to output one or more process states as well as a value for the controlled variable to a differentiator that also takes in a set-point (66, 82) for the controlled variable and the differentiator outputs a control error to the PID controller, wherein the control error represents a difference between the set-point (66, 82) and the value of the controlled variable; wherein: the controlled variable, the set-point (66, 82) for the controlled variable, and the one or more process states are input to the process state augmentation and differentiation module of the machine learning module; the process state augmentation and differentiation module arranges time differences of the one or more process states into a vector and augments the vector with the corresponding control error to produce a time-differentiated and augmented variable; and the machine learning module calculates an instantaneous loss or reward which is an increasing or decreasing, respectively, function of the control error; and the reinforcement module is configured to receive the instantaneous loss or reward, a time difference of control actions, and the time-differentiated and augmented variable, and determines the output provided to the PID controller based on the instantaneous loss or reward, the time difference of control actions, and the time-differentiated and augmented variable. the controlled variable, the set-point (66, 82) for the controlled variable, and the one or more process states are input to the process state augmentation and differentiation module of the machine learning module; the process state augmentation and differentiation module arranges time differences of the one or more process states into a vector and augments the vector with the corresponding control error to produce a time-differentiated and augmented variable; and the machine learning module calculates an instantaneous loss or reward which is an increasing or decreasing, respectively, function of the control error; and the reinforcement module is configured to receive the instantaneous loss or reward, a time difference of control actions, and the time-differentiated and augmented variable, and determines the output provided to the PID controller based on the instantaneous loss or reward, the time difference of control actions, and the time-differentiated and augmented variable.