用人类实验设计课程，让强化学习更好解决3D空间辨识任务
Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design
- 基于人类实验结果设计分阶段学习路径
- 新方法使RL在3D视觉任务中实现有效学习
- 适合研究智能体学习策略与人类认知的学者
强化学习虽在经典游戏和连续控制任务中取得成功，但在复杂、非结构化环境中仍面临挑战。本文研究现代强化学习框架在3D同异判断视觉任务中的表现。尽管使用PPO、行为克隆和模仿学习等先进方法直接训练效果不佳，但通过借鉴真实人类实验结果设计课程学习路径，显著提升了学习效率。该策略使智能体在无先验知识情况下逐步掌握复杂空间关系识别能力，验证了人类认知启发对提升复杂环境学习性能的有效性。
原文摘要 · Abstract (English)
Reinforcement Learning is a mature technology, often suggested as a potential route towards Artificial General Intelligence, with the ambitious goal of replicating the wide range of abilities found in natural and artificial intelligence, including the complexities of human cognition. While RL had shown successes in relatively constrained environments, such as the classic Atari games and specific continuous control problems, recent years have seen efforts to expand its applicability. This work investigates the potential of RL in demonstrating intelligent behaviour and its progress in addressing more complex and less structured problem domains. We present an investigation into the capacity of modern RL frameworks in addressing a seemingly straightforward 3D Same-Different visuospatial task. While initial applications of state-of-the-art methods, including PPO, behavioural cloning and imitation learning, revealed challenges in directly learning optimal strategies, the successful implementation of curriculum learning offers a promising avenue. Effective learning was achieved by strategically designing the lesson plan based on the findings of a real-world human experiment.
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