Yeni bir ilişkisel akıl yürütme test ortamından belirgin ilişkisel bilgileri bir öğrenme ajanı ile çıkarmak

In recent studies, reinforcement learning (RL) agents work in ways that are specialized according to the tasks, and most of the time, their decision-making logic is not interpretable. By using symbolic artificial intelligence techniques like logic programming, statistical methods-based agent algorithms can be enhanced in terms of generalizability and interpretability. In this study, the PrediNet architecture is used for the first time in an RL problem, and in order to perform benchmarking, the multi-head dot-product attention network (MHDPA) was used. By using the PrediNet module, relational information among the objects in the environment can be extracted explicitly. This information is in a form that can be processed in logic programming tools, and the network becomes more interpretable. In order to measure the relational information extraction performances of these two methods, a new test environment, relational-grid-world (RGW), is developed. RGW environment can be generated procedurally from objects with different features, pushing the agent to make complex combinatorial selections in this environment. In the performed tests and the RGW environment, a baseline environment called Box-World is used for comparing both environments and networks separately. The results show that both MHDPA and PrediNet architecture have similar performances in both environments, and the RGW environment is able to measure the relational reasoning capacity of the networks.