视觉工作记忆游戏中基于连续强化学习的动态难度调整
Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game
AI总结：
本研究针对视觉工作记忆游戏提出基于连续强化学习的动态难度调整方法，以连续难度度量和状态-动作设计适配复杂搜索空间，实验证明其较规则方法更能提升玩家体验、得分与胜率，减少得分下滑。
摘要：
动态难度调整（DDA）是提升电子游戏玩家体验的一种可行方法。近年来，强化学习（RL）方法已被应用于非竞技类游戏的DDA中，但这类方法仅依赖搜索空间较小的离散状态-动作空间。本文针对一款视觉工作记忆（VWM）游戏，提出了一种基于连续强化学习的DDA方法，以应对记忆难度的复杂搜索空间。\n所提出的基于RL的DDA根据玩家上一轮的得分和游戏难度来定制游戏难度。我们定义了一种记忆难度的连续度量指标。随后，我们将任务难度作为RL的动作，将难度-得分向量作为RL的状态。\n我们通过一项包含52名受试者的被试内实验对所提方法进行了评估。实验将所提方法与两种基于规则的难度调整方法，在玩家得分和通过问卷测量的游戏体验两个维度上进行了对比。结果显示，所提出的基于RL的方法在胜任感、紧张感以及正负性情绪方面带来了显著更优的游戏体验，玩家也取得了更高的得分和胜率。此外，在20轮的游戏 session 中，所提基于RL的DDA方法带来的得分下降幅度显著更小。
英文摘要：
Dynamic Difficulty Adjustment (DDA) is a viable approach to enhance a player's experience in video games. Recently, Reinforcement Learning (RL) methods have been employed for DDA in non-competitive games; nevertheless, they rely solely on discrete state-action space with a small search space. In this paper, we propose a continuous RL-based DDA methodology for a visual working memory (VWM) game to handle the complex search space for the difficulty of memorization. The proposed RL-based DDA tailors game difficulty based on the player's score and game difficulty in the last trial. We defined a continuous metric for the difficulty of memorization. Then, we consider the task difficulty and the vector of difficulty-score as the RL's action and state, respectively. We evaluated the proposed method through a within-subject experiment involving 52 subjects. The proposed approach was compared with two rule-based difficulty adjustment methods in terms of player's score and game experience measured by a questionnaire. The proposed RL-based approach resulted in a significantly better game experience in terms of competence, tension, and negative and positive affect. Players also achieved higher scores and win rates. Furthermore, the proposed RL-based DDA led to a significantly less decline in the score in a 20-trial session.
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。
