Graph Neural Networks for Reinforcement Learning with Sparse Rewards

Abstract

Reinforcement Learning (RL) has demonstrated remarkable success in various domains, but its application is significantly hindered by the problem of sparse rewards. In environments where rewards are infrequent or delayed, traditional RL algorithms struggle to learn effective policies. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to address this challenge. We introduce a framework where the environment is represented as a graph, and a GNN learns a rich state representation capturing complex relationships between elements. This representation enables the RL agent to better explore and exploit sparse reward signals, ultimately improving learning efficiency and performance. The core of our approach lies in the ability of GNNs to aggregate information from neighboring nodes, effectively encoding contextual dependencies that are crucial for navigating sparse reward landscapes. We demonstrate the effectiveness of this approach through a theoretical analysis and outline the key components of the system, focusing on the integration of GNNs with standard RL algorithms. The goal is to provide a robust and scalable solution for RL problems characterized by sparse rewards.