Targeted Search Control in AlphaZero for Effective Policy Improvement

TL;DR

This paper introduces Go-Exploit, a novel search control strategy for AlphaZero that enhances exploration and value learning by starting self-play from diverse states, leading to improved sample efficiency and stronger gameplay in board games.

Contribution

Go-Exploit's approach of sampling initial states from an archive and shorter trajectories improves exploration, value generalization, and sample efficiency in AlphaZero.

Findings

Go-Exploit outperforms standard AlphaZero in sample efficiency.

It achieves stronger performance against reference opponents.

It surpasses KataGo's sample efficiency and effectiveness.

Abstract

AlphaZero is a self-play reinforcement learning algorithm that achieves superhuman play in chess, shogi, and Go via policy iteration. To be an effective policy improvement operator, AlphaZero's search requires accurate value estimates for the states appearing in its search tree. AlphaZero trains upon self-play matches beginning from the initial state of a game and only samples actions over the first few moves, limiting its exploration of states deeper in the game tree. We introduce Go-Exploit, a novel search control strategy for AlphaZero. Go-Exploit samples the start state of its self-play trajectories from an archive of states of interest. Beginning self-play trajectories from varied starting states enables Go-Exploit to more effectively explore the game tree and to learn a value function that generalizes better. Producing shorter self-play trajectories allows Go-Exploit to train upon more independent value targets, improving value training. Finally, the exploration inherent in Go-Exploit reduces its need for exploratory actions, enabling it to train under more exploitative policies. In the games of Connect Four and 9x9 Go, we show that Go-Exploit learns with a greater sample efficiency than standard AlphaZero, resulting in stronger performance against reference opponents and in head-to-head play. We also compare Go-Exploit to KataGo, a more sample efficient reimplementation of AlphaZero, and demonstrate that Go-Exploit has a more effective search control strategy. Furthermore, Go-Exploit's sample efficiency improves when KataGo's other innovations are incorporated.