Toward Optimal Play of Cribbage

Cribbage is a popular multi-player imperfect-information card game. But despite recent advances in computational methods for imperfect information games, there has been little work on intelligent agents for cribbage. We present an agent for the two-player game based on Monte Carlo Counterfactual Regret Minimization and we describe the simplifications we chose in order to deal with the large state space; the end result achieves results superior to the best agents described in the literature to date.