[2603.26765] Bitboard version of Tetris AI

Computer Science > Artificial Intelligence arXiv:2603.26765 (cs) [Submitted on 24 Mar 2026] Title:Bitboard version of Tetris AI Authors:Xingguo Chen, Pingshou Xiong, Zhenyu Luo, Mengfei Hu, Xinwen Li, Yongzhou Lü, Guang Yang, Chao Li, Shangdong Yang View a PDF of the paper titled Bitboard version of Tetris AI, by Xingguo Chen and 8 other authors View PDF HTML (experimental) Abstract:The efficiency of game engines and policy optimization algorithms is crucial for training reinforcement learning (RL) agents in complex sequential decision-making tasks, such as Tetris. Existing Tetris implementations suffer from low simulation speeds, suboptimal state evaluation, and inefficient training paradigms, limiting their utility for large-scale RL research. To address these limitations, this paper proposes a high-performance Tetris AI framework based on bitboard optimization and improved RL algorithms. First, we redesign the Tetris game board and tetrominoes using bitboard representations, leveraging bitwise operations to accelerate core processes (e.g., collision detection, line clearing, and Dellacherie-Thiery Features extraction) and achieve a 53-fold speedup compared to OpenAI Gym-Tetris. Second, we introduce an afterstate-evaluating actor network that simplifies state value estimation by leveraging Tetris afterstate property, outperforming traditional action-value networks with fewer parameters. Third, we propose a buffer-optimized Proximal Policy Optimization (PPO) algorithm tha...