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In the field of reinforcement learning, tabular methods have become widespread. There are many important scientific results, which significantly improve their performance in specific applications. However, the application of tabular methods is limited due to the large amount of resources required to store value functions in tabular form under high-dimensional state spaces. A natural solution to the memory problem is to use parameterized function approximations. However, conventional approachesdoi:10.20535/2708-4930.1.2020.216042 fatcat:citwm63udnaslfa7hr7jvq2sp4