1 citations · 2 across the 4 of their papers we have counts for
4 papers
Actor-Critic Reinforcement Learning with Phased Actor
Ruofan Wu, Junmin Zhong, Jennie Si
Policy gradient methods in actor-critic reinforcement learning (RL) have become perhaps the most promising approaches to solving continuous optimal control problems. However, the t…
Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning
Junmin Zhong, Ruofan Wu, Jennie Si
We address the issue of estimation bias in deep reinforcement learning (DRL) by introducing solution mechanisms that include a new, twin TD-regularized actor-critic (TDR) method. I…
Modulation-Enhanced Excitation for Continuous-Time Reinforcement Learning via Symmetric Kronecker Products
Brent A. Wallace, Jennie Si
This work introduces new results in continuous-time reinforcement learning (CT-RL) control of affine nonlinear systems to address a major algorithmic challenge due to a lack of per…
Continuous-Time Reinforcement Learning: New Design Algorithms with Theoretical Insights and Performance Guarantees
Brent A. Wallace, Jennie Si
Continuous-time nonlinear optimal control problems hold great promise in real-world applications. After decades of development, reinforcement learning (RL) has achieved some of the…