A View on Deep Reinforcement Learning in Imperfect Information Games

  • T.V. Pricope University of Edinburgh, School of Informatics, Informatics Forum, 10 Crich- ton Street, Edinburgh, UK, EH8 9AB


Many real-world applications can be described as large-scale games of imperfect information. This kind of games is particularly harder than the deterministic one as the search space is even more sizeable. In this paper, I want to explore the power of reinforcement learning in such an environment; that is why I take a look at one of the most popular game of such type, no limit Texas Hold’em Poker, yet unsolved, developing multiple agents with different learning paradigms and techniques and then comparing their respective performances. When applied to no-limit Hold’em Poker, deep reinforcement learning agents clearly outperform agents with a more traditional approach. Moreover, if these last agents rival a human beginner level of play, the ones based on reinforcement learning compare to an amateur human player. The main algorithm uses Fictitious Play in combination with ANNs and some handcrafted metrics. We also applied the main algorithm to another game of imperfect information, less complex than Poker, in order to show the scalability of this solution and the increase in performance when put neck in neck with established classical approaches from the reinforcement learning literature.


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How to Cite
PRICOPE, T.V.. A View on Deep Reinforcement Learning in Imperfect Information Games. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 65, n. 2, p. 31-49, dec. 2020. ISSN 2065-9601. Available at: <https://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/57>. Date accessed: 25 feb. 2021. doi: https://doi.org/10.24193/subbi.2020.2.03.