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Conducting additional search during test time is often used to improve theperformance of reinforcement learning algorithms. Performing search inadversarial games with imperfect information is notoriously difficult and oftenrequires a complicated training process. We present an algorithm that uses anarbitrary policy-gradient algorithm that learns from sampled trajectories inthe setting of fully adversarial two-player games with imperfect information.Alongside the training of the policy network, the algorithm trains anadditional critic network, which provides multiple expected values if bothplayers follow one of a fixed set of transformations of the policy given by thepolicy network. These values are then used for depth-limited search. We showhow the values from this critic can create a value function for imperfectinformation games. Moreover, they can be used to compute the summary statisticsnecessary to start the search from an arbitrary decision point in the game. Thepresented algorithm is scalable to very large games since it does not requireany search in the training time. Furthermore, given sufficient computationalresources, our algorithm may choose whether to use search or play the strategyaccording to the trained policy network anywhere in the game. We evaluate thealgorithm's performance when trained alongside Regularized Nash Dynamics, andwe compare the performance of using the search against the policy network inthe standard benchmark game of Leduc hold'em, multiple variants of imperfectinformation Goofspiel, and in a game of Battleships.

Look-ahead Search on Top of Policy Networks in Imperfect Information Games