用强化学习与行为树结合，让游戏NPC更智能地完成多任务。
Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola
- 将强化学习与行为树协同训练，提升NPC决策能力。
- 在《最后生还者》风格环境中实现多任务自主执行。
- 通过AMD Schola插件验证方案可行性，适合游戏开发团队参考。
尽管强化学习（RL）研究进展迅速，但其在商业游戏中的应用仍很缓慢。本文梳理了游戏AI领域在实际使用RL驱动的非玩家角色（NPC）时面临的常见挑战，并强调强化学习与传统行为树（BT）结合的重要性。尽管已有研究提出该方向，但实际应用极少。我们利用AMD Schola——一个用于在Unreal Engine中训练RL代理的插件，演示了该方法的可行性。在受商业游戏《最后生还者》启发的复杂3D环境中，构建了可执行多种技能的多任务NPC，提供了联合训练RL模型与行为树的详细方法，并展示了多种实用能力。
原文摘要 · Abstract (English)
While the rapid advancements in the reinforcement learning (RL) research community have been remarkable, the adoption in commercial video games remains slow. In this paper, we outline common challenges the Game AI community faces when using RL-driven NPCs in practice, and highlight the intersection of RL with traditional behavior trees (BTs) as a crucial juncture to be explored further. Although the BT+RL intersection has been suggested in several research papers, its adoption is rare. We demonstrate the viability of this approach using AMD Schola -- a plugin for training RL agents in Unreal Engine -- by creating multi-task NPCs in a complex 3D environment inspired by the commercial video game ``The Last of Us". We provide detailed methodologies for jointly training RL models with BTs while showcasing various skills.
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