通过感知预训练，智能体更快掌握流体环境特征并适应多任务
Improving agent performance in fluid environments by perceptual pretraining
- 用信息压缩模型进行无监督预训练，提取流体环境关键特征
- 预训练后智能体在多个场景中适应速度和效果显著提升
- 适合流体控制、机器人导航等需要快速环境感知的领域
本文构建了一个用于流体环境感知的预训练框架，包含一个信息压缩模型及其对应的预训练方法。通过数值模拟在双圆柱问题中进行测试，结果表明，在该框架下进行无监督预训练后，智能体能够获取周围流体环境的关键特征，从而更快速、更有效地适应后续的多场景任务。这些任务包括感知上游障碍物位置以及主动规避流动中的脱落涡旋，以实现减阻目标。性能提升在敏感性分析中得到进一步讨论。
原文摘要 · Abstract (English)
In this paper, we construct a pretraining framework for fluid environment perception, which includes an information compression model and the corresponding pretraining method. We test this framework in a two-cylinder problem through numerical simulation. The results show that after unsupervised pretraining with this framework, the intelligent agent can acquire key features of surrounding fluid environment, thereby adapting more quickly and effectively to subsequent multi-scenario tasks. In our research, these tasks include perceiving the position of the upstream obstacle and actively avoiding shedding vortices in the flow field to achieve drag reduction. Better performance of the pretrained agent is discussed in the sensitivity analysis.
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