📄 中文摘要
高效的游戏引擎和策略优化算法对于在复杂的序列决策任务中训练强化学习（RL）代理至关重要，例如俄罗斯方块。现有的俄罗斯方块实现存在模拟速度慢、状态评估不佳和训练范式效率低下等问题，限制了其在大规模RL研究中的应用。为了解决这些问题，提出了一种基于位板优化和改进RL算法的高性能俄罗斯方块AI框架。通过使用位板表示重新设计俄罗斯方块游戏板和方块，利用位运算加速核心过程（如碰撞检测、行消除和Dellacherie-Thiery特征提取），显著提升了性能。
Tetris AI 的位板版本
出处: Bitboard version of Tetris AI
发布: 2026年3月31日
📄 English Summary
Bitboard version of Tetris AI
The efficiency of game engines and policy optimization algorithms is critical for training reinforcement learning (RL) agents in complex sequential decision-making tasks like Tetris. Existing implementations of Tetris suffer from slow simulation speeds, suboptimal state evaluation, and inefficient training paradigms, which limit their utility for large-scale RL research. A high-performance Tetris AI framework is proposed, based on bitboard optimization and improved RL algorithms. The Tetris game board and tetrominoes are redesigned using bitboard representations, leveraging bitwise operations to accelerate core processes such as collision detection, line clearing, and the extraction of Dellacherie-Thiery features, resulting in significant performance improvements.