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On Combining Decisions from Multiple Expert Imitators for Performance • Agents that attempt to imitate the decisions of an expert (human or artificial)
On Combining Decisions from Multiple Expert Imitators for Performance • Dealing with a collection of expert imitators
Page 1: 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 22 and 23: Methodology • 6 original imitator
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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