Multi-Objective and Constrained Reinforcement Learning for IoT

IoT networks of the future will be characterized by autonomous decision-making by individual devices. Decision-making is done with the purpose of optimizing certain objectives. A multitude of mathematically oriented algorithms exist for solving optimization problems. However, optimization in IoT networks is challenging due to a number of uncertainties, complex network topologies, and rapid changes in the environment. This makes the data-driven and machine learning (ML) approaches more suitable for effectively handling IoT environments’ dynamic and intricate nature. However, supervised and unsupervised ML approaches depend on training data, which is not always available before training. In recent years, reinforcement learning (RL) has attracted considerable attention for solving optimization problems in IoT. This is because RL has the distinguishing feature of learning with experience while interacting with the environment without training data. A central challenge in decision-making in IoT networks is that most optimization problems consist of co-optimizing multiple conflicting objectives. With the development of multi-objective RL (MORL) approaches over the last two decades, there is great potential for utilizing them for future IoT networks. Most recently developed MORL approaches have not been applied in the IoT domain. In this chapter, we will discuss the need for efficient multi-objective optimization in IoT, the fundamentals of using RL for decision-making in IoT, an overview of existing MORL approaches, and, finally, the future scope and challenges associated with utilizing MORL for IoT.