标题： 赢得CityLearn挑战：基于轨迹引导的进化搜索自适应优化 显示英文标题

标题： Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance

摘要： 现代电力系统在未来几年将面临严峻的挑战：由高功率需求高峰引起的城市频繁停电、可再生能源间歇性发电导致的电网不稳定以及全球气候变化因碳排放增加而加剧。 尽管目前的做法越来越不足以应对这些问题，但人工智能（AI）方法的广泛应用仍受到可信度缺失的阻碍。 CityLearn挑战赛为多个学科的研究人员提供了一个极好的机会，以研究人工智能在能源领域解决这些紧迫问题的潜力，这些问题被集体建模为一个强化学习（RL）任务。 当代RL技术面临的多个现实挑战体现在该问题的公式化中。 在本文中，我们提出了一种新颖的方法，使用优化解函数作为策略来计算顺序决策的动作，同时显著地从在线观测中调整优化模型的参数。 算法上，这是通过一种新的基于轨迹引导方案下的进化算法实现的。 形式上，证明了全局收敛性质。 我们的代理在最新的2021年CityLearn挑战赛中排名第一，在几乎所有指标上都表现出卓越的性能，同时保持了某些关键的可解释性方面。 显示英文摘要

摘要： Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions. While current practices are growingly inadequate, the path to widespread adoption of artificial intelligence (AI) methods is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multiple disciplines to investigate the potential of AI to tackle these pressing issues in the energy domain, collectively modeled as a reinforcement learning (RL) task. Multiple real-world challenges faced by contemporary RL techniques are embodied in the problem formulation. In this paper, we present a novel method using the solution function of optimization as policies to compute actions for sequential decision-making, while notably adapting the parameters of the optimization model from online observations. Algorithmically, this is achieved by an evolutionary algorithm under a novel trajectory-based guidance scheme. Formally, the global convergence property is established. Our agent ranked first in the latest 2021 CityLearn Challenge, being able to achieve superior performance in almost all metrics while maintaining some key aspects of interpretability.