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Methodology • 6 original imitator agents • 3 training sets x 2 policies • Derive 2 decision combination players • Ensemble • Dynamic • 8 imitators challenge 2 computerised agents
Match Info • Duplicate Matches • Both player’s receive the same sets of cards • Reduces variance • 3000 duplicate hands • Each imitator plays 5 duplicate matches against each opponent • 1/2 million hands played in each domain
Page 1 and 2: On Combining Decisions from Multipl
Page 3 and 4: On Combining Decisions from Multipl
Page 5 and 6: Domain • Two-Player Texas Hold’
Page 7 and 8: How to combine decisions? • Two e
Page 9 and 10: Intransitive Performance Relationsh
Page 11 and 12: Lazy Learners • Particularly suit
Page 13 and 14: BRIEF ARTICLE Expert Imitators BRIE
Page 15 and 16: Decision Policies • Probabilistic
Page 18 and 19: Showdown DIVAT • As in (Johanson,
Page 20 and 21: Experimental Results
Page 24 and 25: Limit Results Approx. Nash Equilibr
Page 26 and 27: No Limit Results MCTS Rules Table 2
Page 28 and 29: Discussion • Hyperborean-max perf
Page 30: The End.

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