Smooth UCT Search in Computer Poker

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How researchers see this paper

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Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, methodology): “For a more detailed exposition we refer the reader to (Sutton & Barto, 1998), (Myerson, 1991), (Fudenberg, 1998) and (Heinrich et al., 2015).”
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, methodology): “Subsequently, other methods such as Neural Fictitious Self-Play (Heinrich and Silver, 2016) and Monte-Carlo tree search with self-play (Heinrich and Silver, 2015) have also been adopted to successfully solve HULHE.”
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence (2019, background): “Fictitious Self-Play [33, 34, 35] ﬁrst shows promising performance on the competitive game Leduc Poker.”
Fictitious Self-Play in Extensive-Form Games (2015, background): “Despite the popularity of fictitious play to date, it has seen use in few large-scale applications, e.g. (Lambert III et al., 2005; McMahan & Gordon, 2007; Ganzfried & Sandholm, 2009; Heinrich & Silver, 2015).”