智能体通过记忆增强强化学习，学会在动态气流中高效寻味。
Emergence of a Flow-Assisted Casting Strategy for Olfactory Navigation via Memory-Augmented Reinforcement Learning
- 基于记忆增强的强化学习，自适应生成流辅助搜索策略。
- 记忆长度影响寻味速度，存在最优记忆窗口提升成功率。
- 适合研究生物导航、智能机器人路径规划的读者。
在动态流场中，多种动物即使依赖随机嗅觉检测，仍展现出卓越的气味搜寻能力。有趣的是，存在一个最佳的时间窗口用于整合这些检测，以最大化搜索效率。为理解其内在机制，我们研究了强化学习（RL）智能体在非稳态流场中，不同记忆长度和流场条件下的导航表现。无需预设模型，智能体自发发展出一种流辅助的摆动式搜索策略，并自适应调整搜索轨迹几何形态及触发摆动的浓度阈值，以最大化成功概率。智能体平均向气味源移动的速度随记忆长度呈现非单调变化，这一现象可通过‘扇区搜索’模型解释。
原文摘要 · Abstract (English)
In dynamic flow fields, various animals exhibit remarkable odor search capabilities despite relying on stochastic detections. Interestingly, there exists an optimal time window for integrating these detections that maximizes search efficiency. To understand the underlying mechanism, we investigate the navigation performance of Reinforcement Learning (RL) agents in unsteady flows under varying memory lengths and flow conditions. Without any predefined models, the agents develop a flow-assisted casting strategy and adaptively adjust both the geometry of their search trajectories and the concentration threshold for initiating casting to maximize the success rate. The agent's average speed toward the odor source exhibits a non-monotonic dependence on memory length, which can be explained by the "sector-search" model.
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