Abstract

This paper introduces SHA-ZA (Strategic Heuristic Agent with Zero-human Advancement), an advanced reinforcement learning agent trained to master the game of Othello, drawing inspiration from DeepMind's AlphaZero, which achieved exceptional proficiency in chess, shogi, and Go through self-play and reinforcement learning. SHA-ZA employs similar methodologies, utilizing self-play with multiprocessing and Proximal Policy Optimization (PPO) to achieve superior performance without prior human knowledge. Trained on the equivalent of over 650 years of continuous human experience, totaling 33,587,200 games, SHA-ZA underwent rigorous testing against diverse opponents, resulting in significant strategic gameplay advancements. The findings illustrate SHA-ZA's ability to surpass advanced-level minimax engines, highlighting the effectiveness of combining PPO and self-play for mastering complex board games like Othello.