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Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022. [ABSTRACT FROM AUTHOR]

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users’ tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

This paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller.

This paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller. [ABSTRACT FROM AUTHOR]