… 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 …

… 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…

… Due to their versatility across applications, the DDPG and the SAC controllers are … costs,
and they significantly exceed other benchmark controllers in terms of minimizing the total …

… 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 …

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

… applications [8], PowerGridWorld for power systems [9], and CityLearn for demand response
… RL environments and algorithms for both EV charging [11–13] and electricity markets [14–16…

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