2 code implementations • 14 Sep 2023 • Xinyi Zhao, Chaoyue Zhao, Grace Jia
The transition from traditional bus fleets to zero-emission ones necessitates the development of effective planning models for battery electric bus (BEB) charging infrastructure.
no code implementations • 25 Jun 2023 • Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning.
no code implementations • 24 Jun 2023 • Jun Song, Chaoyue Zhao
Demand response (DR) has been demonstrated to be an effective method for reducing peak load and mitigating uncertainties on both the supply and demand sides of the electricity market.
no code implementations • 24 Jun 2023 • Jun Song, William Yang, Chaoyue Zhao
In this paper, we present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem.
no code implementations • 4 Jun 2022 • YuXuan Li, Chaoyue Zhao, Chenang Liu
Although traditional optimization techniques, such as stochastic and robust optimization approaches, could be leveraged to address the OPF problem, in the face of renewable energy uncertainty, i. e., the dynamic coefficients in the optimization model, their effectiveness in dealing with large-scale problems remains limited.
no code implementations • 29 Sep 2021 • Jun Song, Chaoyue Zhao, Niao He
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning.
1 code implementation • 14 Jun 2020 • Jun Song, Chaoyue Zhao
Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), as the widely employed policy based reinforcement learning (RL) methods, are prone to converge to a sub-optimal solution as they limit the policy representation to a particular parametric distribution class.