Knowledge Graph Injection for Reinforcement Learning

In reinforcement learning (RL) an agent usually learns the specifics and rules of the environment via interaction. This limits the agent in taking the best action only from the current observation and past experience. Therefore, providing relevant external knowledge for RL agents, as well as incorporating learned knowledge in the RL process can be a critical part of agent’s successful training in real-world tasks. We propose a method, an architecture and experimental results for injecting expert knowledge in the form of RDF knowledge graphs (KGs) into the RL processes, showing how knowledge consumption increases sample efficiency. Furthermore, we investigate the scalability of our approach concerning the complexity of the underlying task showing injection of KGs is beneficial to the solution of more complex RL tasks. For experimental evaluation we used the Minigrid environment, which is a standard benchmark for RL. For this environment, we designed an ontology and generated a KG, that promotes reusability and interoperability across heterogeneous data of the environment. We show that adding knowledge to the agent’s learning process improves sample efficiency and the benefits increase with the complexity of the environment