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Reinforcement learning (RL) agents are often designed specifically for aparticular problem and they generally have uninterpretable working processes.Statistical methods-based agent algorithms can be improved in terms ofgeneralizability and interpretability using symbolic Artificial Intelligence(AI) tools such as logic programming. In this study, we present a model-free RLarchitecture that is supported with explicit relational representations of theenvironmental objects. For the first time, we use the PrediNet networkarchitecture in a dynamic decision-making problem rather than image-basedtasks, and Multi-Head Dot-Product Attention Network (MHDPA) as a baseline forperformance comparisons. We tested two networks in two environments ---i.e.,the baseline Box-World environment and our novel environment,Relational-Grid-World (RGW). With the procedurally generated RGW environment,which is complex in terms of visual perceptions and combinatorial selections,it is easy to measure the relational representation performance of the RLagents. The experiments were carried out using different configurations of theenvironment so that the presented module and the environment were compared withthe baselines. We reached similar policy optimization performance results withthe PrediNet architecture and MHDPA; additionally, we achieved to extract thepropositional representation explicitly ---which makes the agent's statisticalpolicy logic more interpretable and tractable. This flexibility in the agent'spolicy provides convenience for designing non-task-specific agentarchitectures. The main contributions of this study are two-fold ---an RL agentthat can explicitly perform relational reasoning, and a new environment thatmeasures the relational reasoning capabilities of RL agents.

Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction