回合制游戏中的推理学习：第一人称射击游戏中购买决策的多任务序列生成
Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters
摘要：
顺序推理是一种复杂的人类能力，以前的广泛研究集中在单个连续游戏中的游戏AI，基于回合的决策扩展到一系列游戏仍然很少探索。反恐精英：《全球进攻》（CS：GO）作为一款回合制游戏，拥有丰富的专家演示，为多人回合顺序推理提供了一个良好的环境。在这项工作中，我们提出了一个序列推理轮属性编码器和多任务解码器来解释基于轮的购买决策背后的策略。我们采用少样本学习来对一场比赛中的多轮进行采样，并修改模型无关的Meta学习算法Reptile来实现元学习循环。我们制定每一轮作为一个多任务序列生成问题。我们的状态表示结合了动作编码器、团队编码器、玩家特征、回合属性编码器和经济编码器，以帮助我们的智能体在这种特定的多玩家回合场景下学习推理。一个完整的消融研究和比较贪婪的方法证明我们的模型的有效性。我们的研究将为可解释的人工智能打开大门，以了解游戏社区以外的情景和长期购买策略。
英文摘要：
Sequential reasoning is a complex human ability, with extensive previous research focusing on gaming AI in a single continuous game, round-based decision makings extending to a sequence of games remain less explored. Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundant expert demonstrations, provides an excellent environment for multi-player round-based sequential reasoning. In this work, we propose a Sequence Reasoner with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies behind the round-based purchasing decisions. We adopt few-shot learning to sample multiple rounds in a match, and modified model agnostic meta-learning algorithm Reptile for the meta-learning loop. We formulate each round as a multi-task sequence generation problem. Our state representations combine action encoder, team encoder, player features, round attribute encoder, and economy encoders to help our agent learn to reason under this specific multi-player round-based scenario. A complete ablation study and comparison with the greedy approach certify the effectiveness of our model. Our research will open doors for interpretable AI for understanding episodic and long-term purchasing strategies beyond the gaming community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
