… Most studies lack proper benchmarking compared to model-based approaches or gray-box …
RL applications in demand response as well as building control problems related to demand…

… , and benchmarking of reinforcement learning algorithms, we launched The CityLearn Challenge
2020, an international challenge of reinforcement learning … , CityLearn uses a different …

… Our proposed RL-based DR algorithm is benchmarked against … with the benchmark solutions,
our proposed algorithm can not … Inspired by the application of RL in energy scheduling and …

… algorithm with decentralized multi-agent RL methodology, a benchmark without learning is
… This benchmark uses the exact model and knows all information of the system, to minimize …

… DRL-based demand response algorithm was assessed in comparison with several benchmarks.
… His research interests include deep learning algorithm design and its application in time…

… Furthermore, the algorithm benchmark has been carried using an open source co-… The
rule-based algorithm creates and uses a single instance of a HTTP client class during its …

… benchmark without demand response. Moreover, the performance of the multi-agent deep
reinforcement learning … seen broader adoption for machine learning applications, and it …

… which utilize machine learning and optimization algorithms. … ) transfer learning by providing
researchers with benchmark … applied transfer learning to demand response applications. …

… still a lack of models related to demand response, which results in … uses the Weber–Fechner
law and a clustering algorithm to … Compared to the benchmark model, the demand response …

… scheduling algorithm based on deep reinforcement learning (… The proposed approach is
benchmarked against two widely … , machine learning, data mining, and various applications. He …