@inproceedings{2023_4C_CL,
  title={Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance},
  author={Vanshaj Khattar and  Ming Jin},
  booktitle={AAAI Conference on Artificial Intelligence (AAAI) AI for Social Impact Track},
  pages={},
  year={2023},
  url_arXiv={https://arxiv.org/abs/2212.01939},
  url_pdf={ESGuidance_ZOiRL.pdf},
  keywords = {Optimization, Power system, Reinforcement learning, Machine Learning},
  abstract={Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high peaks of electricity demand, grid instability exacerbated by the intermittency of renewable generation, and climate change on a global scale amplified by increasing carbon emissions. While current practices are growingly inadequate, the pathway of artificial intelligence (AI)-based methods to widespread adoption is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multi-disciplinary fields to investigate the potential of AI to tackle these pressing issues within 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 the 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.  },
}

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While current practices are growingly inadequate, the pathway of artificial intelligence (AI)-based methods to widespread adoption is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multi-disciplinary fields to investigate the potential of AI to tackle these pressing issues within 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 the 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. ","bibtex":"@inproceedings{2023_4C_CL,\n title={Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance},\n author={Vanshaj Khattar and Ming Jin},\n booktitle={AAAI Conference on Artificial Intelligence (AAAI) AI for Social Impact Track},\n pages={},\n year={2023},\n url_arXiv={https://arxiv.org/abs/2212.01939},\n url_pdf={ESGuidance_ZOiRL.pdf},\n keywords = {Optimization, Power system, Reinforcement learning, Machine Learning},\n abstract={Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high peaks of electricity demand, grid instability exacerbated by the intermittency of renewable generation, and climate change on a global scale amplified by increasing carbon emissions. While current practices are growingly inadequate, the pathway of artificial intelligence (AI)-based methods to widespread adoption is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multi-disciplinary fields to investigate the potential of AI to tackle these pressing issues within 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 the 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. },\n}\n\n\n","author_short":["Khattar, V.","Jin, M."],"key":"2023_4C_CL","id":"2023_4C_CL","bibbaseid":"khattar-jin-winningthecitylearnchallengeadaptiveoptimizationwithevolutionarysearchundertrajectorybasedguidance-2023","role":"author","urls":{" arxiv":"https://arxiv.org/abs/2212.01939"," pdf":"http://www.jinming.tech/papers/ESGuidance_ZOiRL.pdf"},"keyword":["Optimization","Power system","Reinforcement learning","Machine Learning"],"metadata":{"authorlinks":{}},"downloads":25},"bibtype":"inproceedings","biburl":"http://www.jinming.tech/papers/myref.bib","dataSources":["sTzDHHaipTZWjp8oe","Y64tp2HnDCfXgLdc5"],"keywords":["optimization","power system","reinforcement learning","machine learning"],"search_terms":["winning","citylearn","challenge","adaptive","optimization","evolutionary","search","under","trajectory","based","guidance","khattar","jin"],"title":"Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance","year":2023,"downloads":25}