标题： 使用强化学习的趋化运动 显示英文标题

标题： Run-and-tumble chemotaxis using reinforcement learning

摘要： 细菌细胞通过运行和翻滚运动在环境中沿着吸引物浓度梯度向上移动。通过延长向上运行时间和缩短向下运行时间，细胞向高吸引物区域迁移。受此启发，我们制定了一种强化学习（RL）算法，其中智能体在存在吸引物梯度的一维环境中移动。智能体可以执行两种动作：要么沿同一方向持续运动，要么改变方向。我们根据智能体轨迹的近期历史为这些动作分配成本。我们提出的问题是：在不同类型的吸引物环境中，哪种RL策略效果最好。我们通过智能体的能力来量化RL策略的效率，（a）在长时间后定位在有利区域，以及（b）了解其完整环境。根据吸引物分布和初始条件，我们发现需要在探索和利用之间找到最佳平衡，以确保最高效的性能。 显示英文摘要

摘要： Bacterial cells use run-and-tumble motion to climb up attractant concentration gradient in their environment. By extending the uphill runs and shortening the downhill runs the cells migrate towards the higher attractant zones. Motivated by this, we formulate a reinforcement learning (RL) algorithm where an agent moves in one dimension in the presence of an attractant gradient. The agent can perform two actions: either persistent motion in the same direction or reversal of direction. We assign costs for these actions based on the recent history of the agent's trajectory. We ask the question: which RL strategy works best in different types of attractant environment. We quantify efficiency of the RL strategy by the ability of the agent (a) to localize in the favorable zones after large times, and (b) to learn about its complete environment. Depending on the attractant profile and the initial condition, we find an optimum balance is needed between exploration and exploitation to ensure the most efficient performance.