Search Results for author: Chaoyue Zhao

Found 7 papers, 2 papers with code

Grid-Aware On-Route Fast-Charging Infrastructure Planning for Battery Electric Bus with Equity Considerations: A Case Study in South King County

2 code implementations14 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.

Fairness

Provably Convergent Policy Optimization via Metric-aware Trust Region Methods

no code implementations25 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.

Continuous Control Policy Gradient Methods

Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach

no code implementations24 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.

Reinforcement Learning (RL)

Decision-Dependent Distributionally Robust Markov Decision Process Method in Dynamic Epidemic Control

no code implementations24 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.

Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow

no code implementations4 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.

Computational Efficiency Generative Adversarial Network

Efficient Wasserstein and Sinkhorn Policy Optimization

no code implementations29 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.

Policy Gradient Methods Reinforcement Learning (RL)

Optimistic Distributionally Robust Policy Optimization

1 code implementation14 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.

reinforcement-learning Reinforcement Learning (RL)

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