40 citations · 49 across the 7 of their papers we have counts for
7 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…
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
Ruofan Wu, Jiawei Qiao, Mingzhe Wu +5
We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative…
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems
Junmin Zhong, Ruofan Wu, Jennie Si
High variances in reinforcement learning have shown impeding successful convergence and hurting task performance. As reward signal plays an important role in learning behavior, mul…
Robotic Knee Tracking Control to Mimic the Intact Human Knee Profile Based on Actor-critic Reinforcement Learning
Ruofan Wu, Zhikai Yao, Jennie Si +2
We address a state-of-the-art reinforcement learning (RL) control approach to automatically configure robotic prosthesis impedance parameters to enable end-to-end, continuous locom…
Reinforcement Learning Enabled Automatic Impedance Control of a Robotic Knee Prosthesis to Mimic the Intact Knee Motion in a Co-Adapting Environment
Ruofan Wu, Minhan Li, Zhikai Yao +3
Automatically configuring a robotic prosthesis to fit its user's needs and physical conditions is a great technical challenge and a roadblock to the adoption of the technology. Pre…
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems
Zhikai Yao, Jennie Si, Ruofan Wu +1
This paper addresses reinforcement learning based, direct signal tracking control with an objective of developing mathematically suitable and practically useful design approaches.…