Bin Hu

activity

20172022

most citedDerivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

8 citations · 35 across the 12 of their papers we have counts for

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Showing 2021Show all

2 papers · 1 filter

cs.LG2021

Model-Free $μ$ Synthesis via Adversarial Reinforcement Learning

Darioush Keivan, Aaron Havens, Peter Seiler +2

Motivated by the recent empirical success of policy-based reinforcement learning (RL), there has been a research trend studying the performance of policy-based RL methods on standa…

math.OC2021★ 8 cited

Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

Kaiqing Zhang, Xiangyuan Zhang, Bin Hu +1

Direct policy search serves as one of the workhorses in modern reinforcement learning (RL), and its applications in continuous control tasks have recently attracted increasing atte…

Model-Free μ Synthesis via Adversarial Reinforcement Learning

Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

Model-Free $μ$ Synthesis via Adversarial Reinforcement Learning