塔防游戏中高层策略控制的强化学习
Reinforcement Learning for High-Level Strategic Control in Tower Defense Games
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中文总结 AI 辅助
本文提出将强化学习与脚本化AI相结合用于塔防游戏测试与验证，并在《植物大战僵尸》上验证该方法比纯启发式AI成功率更高、更稳健。
中文摘要 AI 辅助
在策略游戏中，游戏设计最重要的方面之一是保持对玩家的挑战感。许多移动端游戏具有快速的玩法循环，使玩家能够稳步推进，这需要大量的关卡和谜题来防止玩家过快地到达终点。与任何内容创作一样，测试和验证对于确保引人入胜的游戏机制、令人愉悦的游戏资源和可玩的关卡至关重要。在本文中，我们提出了一种可用于游戏测试和验证的自动化方法，该方法将传统的脚本化方法与强化学习相结合，既获得了两种方法的优势，又能像人类玩家一样适应新情况。我们在流行的塔防游戏《植物大战僵尸》上测试了我们的解决方案。结果表明，将强化学习等学习型方法与脚本化AI相结合，能够产生比仅使用启发式AI性能更高、更稳健的智能体，在40个关卡中取得了57.12%的成功率，而后者为47.95%。此外，结果还表明，为这类益智型游戏训练一个通用智能体存在较大难度。
英文摘要
In strategy games, one of the most important aspects of game design is maintaining a sense of challenge for players. Many mobile titles feature quick gameplay loops that allow players to progress steadily, requiring an abundance of levels and puzzles to prevent them from reaching the end too quickly. As with any content creation, testing and validation are essential to ensure engaging gameplay mechanics, enjoyable game assets, and playable levels. In this paper, we propose an automated approach that can be leveraged for gameplay testing and validation that combines traditional scripted methods with reinforcement learning, reaping the benefits of both approaches while adapting to new situations similarly to how a human player would. We test our solution on a popular tower defense game, Plants vs. Zombies. The results show that combining a learned approach, such as reinforcement learning, with a scripted AI produces a higher-performing and more robust agent than using only heuristic AI, achieving a 57.12% success rate compared to 47.95% in a set of 40 levels. Moreover, the results demonstrate the difficulty of training a general agent for this type of puzzle-like game.
发表机构
- Electronic Arts (EA)（美国艺电公司（EA））
机构由 AI 辅助整理，请以论文原文为准。
