Smart Stormwater Control Systems: A Reinforcement Learning Approach
Smart Stormwater Control Systems: A Reinforcement Learning Approach
复制标题
智能雨水控制系统：强化学习方法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Tavakoli
中科院分区：
文献类型：
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作者：
Cheng Wang;A. Tavakoli
洪水对许多城市地区构成了重大且日益严重的风险。雨水系统通常用于控制洪水，但传统上是被动的(即没有可控部件)。然而，如果雨水系统改装了阀门和水泵，就可以实施实时控制的政策，以在比最初设计的更广泛的条件下提高系统的性能。在这篇文章中，我们提出了一种基于自主强化学习(RL)的暴雨控制系统，旨在最大限度地减少风暴期间的洪水。通过这种方法，可以通过让RL代理响应于接收到的奖励信号与系统交互来学习最优控制策略。与一组静态控制规则相比，RL在大范围的人工风暴事件中表现出更好的性能。这证明了RL基于观察和互动学习控制行动的能力，这对动态和不断变化的城市地区来说是一个关键好处。
Flooding poses a significant and growing risk for many urban areas. Stormwater systems are typically used to control flooding, but are traditionally passive (i.e. have no controllable components). However, if stormwater systems are retrofitted with valves and pumps, policies for controlling them in real-time could be implemented to enhance system performance over a wider range of conditions than originally designed for. In this paper, we propose an autonomous, reinforcement learning (RL) based, stormwater control system that aims to minimize flooding during storms. With this approach, an optimal control policy can be learned by letting an RL agent interact with the system in response to received reward signals. In comparison with a set of static control rules, RL shows superior performance on a wide range of artificial storm events. This demonstrates RL’s ability to learn control actions based on observation and interaction, a key benefit for dynamic and ever-changing urban areas.