A Practical Use of Imperfect Recall

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How researchers see this paper

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Computer poker: A review (2011, methodology): “[97] present results that suggest this is not an issue in practise.”
Evaluating state-space abstractions in extensive-form games (2013, methodology): “The use of imperfect recall abstractions in the poker domain was first proposed by Waugh et al. [19].”
Endgame Solving in Large Imperfect-Information Games (2015, background): “Imperfect-recall abstractions allow for greater flexibility in which hands can be grouped together, and significantly improve performance over perfect-recall abstractions [20, 13].”
Finding Optimal Abstract Strategies in Extensive-Form Games (2012, methodology): “However, Waugh et al. have shown that different abstract game equilibria can have a wide range of exploitability (Waugh et al. 2009a, Table 3), making this approach unreliable.”
Tractable Objectives for Robust Policy Optimization (2012, background): “Abstraction has proven useful for finding good policies in other large extensive-form-games [2, 18], and so will likely prove effective here.”
Hierarchical Abstraction, Distributed Equilibrium Computation, and Post-Processing, with Application to a Champion No-Limit Texas Hold'em Agent (2015, methodology): “with imperfect recall have consistently been shown to outperform agents that use perfect recall abstractions [26].”