Relational Reinforcement Learning with Graph Neural Networks for Robotics

Abstract

This paper presents a novel approach to robot control in complex, multi-agent environments by leveraging relational reinforcement learning (RRL) and graph neural networks (GNNs). The core idea is that robots benefit significantly from understanding and reasoning about the relationships between themselves and their surroundings. Traditional reinforcement learning methods often struggle in scenarios where the reward signal is not directly tied to individual actions but depends on the overall state and interactions within a system. Our proposed framework addresses this limitation by employing a GNN to explicitly model the relationships between robots and environmental elements. This graph representation allows the GNN to capture contextual information and relational dependencies, which are then used by the RRL algorithm to learn optimal policies. We demonstrate the effectiveness of this approach through a theoretical analysis and a conceptual framework, highlighting its potential to improve robot performance in scenarios demanding sophisticated relational reasoning. This work establishes a foundation for future research in combining relational reasoning with deep learning techniques for robust and adaptive robot control.