论文标题
神经MMO v1.3：一个大量的多基因游戏环境，用于培训和评估神经网络
Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
论文作者
论文摘要
多基因情报研究的进展在根本上受到可用于研究的环境的数量和质量的限制。近年来，模拟游戏已成为强化学习中的主要研究平台，部分原因是它们的可及性和解释性。先前的作品针对并在Arcade，第一人称射击游戏（FPS），实时策略（RTS）和大型在线战场（MOBA）游戏上取得了成功。我们的工作考虑了大量多人在线角色扮演游戏（MMORPGS或MMO），这些游戏捕获了几种现实世界中的复杂性，这些复杂性并非任何其他游戏类型都无法很好地建模。我们提出了神经MMO，这是一个受MMO启发的大量多种游戏环境，并讨论了我们在AI研究的多种系统系统工程方面的两个总体挑战：分布式基础架构和游戏IO中的两个总体挑战。我们进一步证明，标准的策略梯度方法和简单的基线模型可以在这种情况下学习有趣的新兴探索和专业化行为。
Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targeted and demonstrated success on arcade, first person shooter (FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games. Our work considers massively multiplayer online role-playing games (MMORPGs or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO. We further demonstrate that standard policy gradient methods and simple baseline models can learn interesting emergent exploration and specialization behaviors in this setting.
