no code implementations • 23 May 2024 • Minheng Xiao, Xian Yu, Lei Ying
However, developing policy gradient methods for risk-sensitive DRL is inherently more complex as it pertains to finding the gradient of a probability measure.
Distributional Reinforcement Learning Policy Gradient Methods +1
no code implementations • 7 Mar 2023 • Xian Yu, Xiaozhu Fang, Biqiang Mu, Tianshi Chen
For data-driven iterative learning control (ILC) methods, both the model estimation and controller design problems are converted to parameter estimation problems for some chosen model structures.
no code implementations • 26 Jan 2023 • Xian Yu, Lei Ying
Risk-sensitive reinforcement learning (RL) has become a popular tool to control the risk of uncertain outcomes and ensure reliable performance in various sequential decision-making problems.
no code implementations • 14 Jan 2023 • Xian Yu, Siqian Shen
We adapt the Expected Conditional Risk Measures (ECRMs) to the infinite-horizon risk-averse MDP and prove its time consistency.
no code implementations • 20 Oct 2020 • Gongyu Chen, Xinyu Fei, Huiwen Jia, Xian Yu, Siqian Shen
The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools, workplaces and communities to return to operations during the pandemic, requiring policymakers to balance individuals' safety and operational efficiency.