用神经演化让AI从像素中学会黑暗之魂战斗
Learning Dark Souls Combat Through Pixel Input With Neuroevolution
- 直接从原始像素进化神经网络，无需游戏状态信息
- 在无预设策略下击败初始Boss，成功率达35%
- 适合研究视觉驱动的智能体或缺乏接口的游戏
本文研究了通过神经演化增强拓扑结构（NEAT）自动化《黑暗之魂》游戏进程的方法。该游戏以复杂的战斗机制、动态环境和高维视觉输入著称。与传统强化学习不同，该方法直接从原始像素数据演化神经网络，避免依赖显式游戏状态信息。为此，我们提出Dark Souls API（DSAPI），一个基于实时计算机视觉的Python框架，用于提取玩家与敌方生命值等关键游戏指标。利用NEAT，智能体在无预设行为或领域启发式的情况下，进化出有效对抗初始首领‘疯人院恶魔’的战斗策略。实验结果表明，所演化智能体最高达成35%的成功率，证明了视觉驱动神经演化在复杂、视觉密集型游戏场景中的可行性。本工作展示了该方法在缺乏直接API支持或明确状态表示的挑战性游戏环境中的潜力。
原文摘要 · Abstract (English)
This paper investigates the application of Neuroevolution of Augmenting Topologies (NEAT) to automate gameplay in Dark Souls, a notoriously challenging action role-playing game characterized by complex combat mechanics, dynamic environments, and high-dimensional visual inputs. Unlike traditional reinforcement learning or game playing approaches, our method evolves neural networks directly from raw pixel data, circumventing the need for explicit game-state information. To facilitate this approach, we introduce the Dark Souls API (DSAPI), a novel Python framework leveraging real-time computer vision techniques for extracting critical game metrics, including player and enemy health states. Using NEAT, agents evolve effective combat strategies for defeating the Asylum Demon, the game's initial boss, without predefined behaviors or domain-specific heuristics. Experimental results demonstrate that evolved agents achieve up to a 35% success rate, indicating the viability of neuroevolution in addressing complex, visually intricate gameplay scenarios. This work represents an interesting application of vision-based neuroevolution, highlighting its potential use in a wide range of challenging game environments lacking direct API support or well-defined state representations.
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