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The Big Match in Small Space (Extended Abstract)

Publication at Faculty of Mathematics and Physics |
2016

Abstract

We study repeated games with absorbing states, a type of two-player, zero-sum concurrent mean-payoff games with the prototypical example being the Big Match of Gillete (1957). These games may not allow optimal strategies but they always have epsilon-optimal strategies.

In this paper we design epsilon-optimal strategies for Player 1 in these games that use only O(log log T) space. Furthermore, we construct strategies for Player 1 that use space s(T), for an arbitrary small unbounded non-decreasing function s, and which guarantee an epsilon-optimal value for Player 1 in the limit superior sense.

The previously known strategies use space Omega(log T) and it was known that no strategy can use constant space if it is epsilon-optimal even in the limit superior sense. We also give a complementary lower bound.

Furthermore, we also show that no Markov strategy, even extended with finite memory, can ensure value greater than 0 in the Big Match, answering a question posed by Neyman [11].