SmooCT
1 July 2014
SmooCT from University College London (UCL), games model published 2014-07-01. Notable for: sota improvement.
Abstract
Self-play reinforcement learning has proved to be successful in many perfect information two-player games. However, research carrying over its theoretical guarantees and practical success to games of imperfect information has been lacking. In this paper, we evaluate selfplay Monte-Carlo Tree Search (MCTS) in limit Texas Hold’em and Kuhn poker. We introduce a variant of the established UCB algorithm and provide first empirical results demonstrating its ability to find approximate Nash equilibria.
Sources: Epoch AI. This entry was generated from a dataset and has not been edited by hand.