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A View on Deep Reinforcement Learning in Imperfect Information Games
2020
Studia Universitatis Babes-Bolyai: Series Informatica
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
doi:10.24193/subbi.2020.2.03
fatcat:5umr7wvphba23ltwqibeotyqau