Enabling Relational Reasoning in Multi-Agent Deep Reinforcement Learning

The aim of reinforcement learning is to teach an artificial agent how to take optimal sequential decisions in an uncertain environment to complete a task. Recent advancements in the field have leveraged deep learning methods as function approximators thus enabling complex applications in various areas, from gaming to bioinformatics. Despite these advances, most of the work has focused on the case of a single agent interacting with the environment. However, many real-world applications involve multiple cooperative agents taking joint decisions; some prominent examples include autonomous vehicles, manufacturing robotics and cyber-security bots. In such settings, inter-agent communication becomes essential to achieve collaborative behaviour, and recent developments in the fields have been concerned with facilitating the spontaneous emergence of communication protocols throughout the learning process. In this project, we will develop a modelling framework where, in addition to learning how to communicate, the agents can also develop the ability to perform relational reasoning, i.e. they'll be able to infer how the entities acting in the environment are related to one another and encode those relationships in order to improve the decision-making process. In doing so, we will draw heavily from the field of geometric deep learning where relational graph neural networks are currently employed to learn relational patterns from network-valued data. Our aim is to develop a unified relational reinforcement learning approach for multi agent systems that is both decentralised and scalable. Several applications of increasing complexity will be considered to showcase the potential use of our algorithms in real-world use cases.