84 citations · 139 across the 10 of their papers we have counts for
10 papers
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…
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.…