AI Game Strategy Mastery: Lessons from AlphaZero in Gomoku

Abstract

This paper explores the game strategy mastery demonstrated by the AlphaZero artificial intelligence system in the game of Gomoku. AlphaZero achieved superhuman performance in Gomoku by starting with random play and improving solely through self-play reinforcement learning. We analyze the evolution of AlphaZero's game strategies and extract key lessons that contributed to its mastery. First, AlphaZero mastered the opening book theory by deducing strong initial stone placements that maximize flexibility. Second, it developed strategies to constrain its opponent's responses during the opening phase. Third, AlphaZero balanced offensive and defensive considerations, recognizing that sometimes allowing weaknesses can enable greater tactical opportunities later. Fourth, it identified recurring shape patterns and leveraged them for efficient heuristic evaluations. Fifth, AlphaZero exhibited long-term planning and strategic sacrifice of pieces to gain an advantage. We complement the strategic analysis with econometric models that quantify AlphaZero's improvement in game parameters like win rate, game length, and advantage over time. The insights gained from studying expert game playing systems like AlphaZero can potentially be transferred to improve strategy and planning in other multifaceted domains like business, politics, and military operations.