Accelerating Best Response Calculation in Large Extensive Games

By Michael Bradley Johanson, Kevin Waugh, Michael H. Bowling, Martin A. Zinkevich. 2011

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Citations in the corpus

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning (2017, background): “NashConv is easy to compute in small enough games [45]; for CFR’s values see Appendix E.”
A survey and critique of multiagent deep reinforcement learning (2018, background): “[254] also found “overfitting” when solving large extensive games (e.”
Rethinking Formal Models of Partially Observable Multiagent Decision Making (2019, methodology): “he ideas related to decomposition in games with imperfect information have been developed in the EFG framework (augmented information sets, public states, and subgames, deﬁned in (Burch et al., 2014; Johanson et al., 2011; Moravˇcík et al., 2017; Šustr et al., 2019)), this has been done in a patchwo”
Finding Optimal Abstract Strategies in Extensive-Form Games (2012, methodology): “A recent publication presented a technique for efficiently computing best-responses in very large extensiveform games (Johanson et al. 2011).”
Time and Space: Why Imperfect Information Games are Hard (2018, background): “Because there can be a disconnect between exploitability and the one-on-one performance [47], we might also be interested in the effect of re-solving on the expected performance against other agents.”
Solving Games with Functional Regret Estimation (2014, background): “2008), it appears to not occur with any significance in the large games of interest (Johanson et al. 2011; Bard et al. 2013).”
Evaluating state-space abstractions in extensive-form games (2013, methodology): “The Percentile nested E[HS 2 ] / E[HS] abstraction technique has been well studied by researchers [12] [19] and was used by Hyperborean in the Annual Computer Poker Competitions from 2007 to 2009.”
Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks (2015, background): “2007) (Johanson et al. 2011) (Johanson et al.”
Measuring the Size of Large No-Limit Poker Games (2013, background): “While heads-up limit is sufficiently small that the suboptimality of strategies can now be evaluated conveniently [8] and close approximations to an optimal strategy are becoming possible [6], the situation in the no-limit ACPC events appears bleak.”