个性化动态难度调整——模仿学习与强化学习的结合
Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning
AI总结：
针对电子游戏难度与玩家技能不匹配影响体验的问题，提出结合模仿学习与强化学习的双智能体框架，用于格斗游戏场景下的个性化动态难度调整。
摘要：
在电子游戏中平衡游戏难度是为玩家打造有趣游戏体验的核心任务。游戏难度与玩家的技能或投入度不匹配会导致玩家产生挫败感或感到无聊，进而减少游戏时长。本研究探索使用基于机器学习的智能体来平衡游戏难度，根据玩家当前行为为其提供适配的挑战。该方法通过两个智能体的组合实现：一个智能体学习模仿玩家，第二个智能体则被训练来击败前者。在我们的演示中，我们在格斗游戏AI竞赛的场景下，针对所提出的个性化AI智能体动态难度调整框架展开了研究。
英文摘要：
Balancing game difficulty in video games is a key task to create interesting gaming experiences for players. Mismatching the game difficulty and a player's skill or commitment results in frustration or boredom on the player's side, and hence reduces time spent playing the game. In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.
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。
