Memorizing the Playout Policy

Abstract

Monte Carlo Tree Search (MCTS) is the state-of-the-art algorithm for General Game Playing (GGP). Playout Policy Adaptation with move Features (PPAF) is a state-of-the-art MCTS algorithm that learns a playout policy online. We propose a simple modification to PPAF consisting in memorizing the learned policy from one move to the next. We test PPAF with memorization (PPAFM) against PPAF and UCT for Atarigo, Breakthrough, Misere Breakthrough, Domineering, Misere Domineering, Knightthrough, Misere Knightthrough and Nogo.