8 papers
Real-Time Diffusion Policies for Games: Enhancing Consistency Policies with Q-Ensembles
Ruoqi Zhang, Ziwei Luo, Jens Sjölund +3
Diffusion models have shown impressive performance in capturing complex and multi-modal action distributions for game agents, but their slow inference speed prevents practical depl…
Convergence in On-line Learning of Static and Dynamic Systems
Torbjörn Wigren, Ruoqi Zhang, Per Mattsson
The paper derives analytical expressions for the asymptotic average updating direction of the adaptive moment generation (ADAM) algorithm when applied to recursive identification o…
Safe Output Feedback Improvement with Baselines
Ruoqi Zhang, Per Mattsson, Dave Zachariah
In data-driven control design, an important problem is to deal with uncertainty due to limited and noisy data. One way to do this is to use a min-max approach, which aims to minimi…
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
Ruoqi Zhang, Ziwei Luo, Jens Sjölund +2
This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL). At the core is a mean-reverting stochastic differential equation (SD…
Risk-sensitive Actor-free Policy via Convex Optimization
Ruoqi Zhang, Jens Sjölund
Traditional reinforcement learning methods optimize agents without considering safety, potentially resulting in unintended consequences. In this paper, we propose an optimal actor-…
Aiding reinforcement learning for set point control
Ruoqi Zhang, Per Mattsson, Torbjörn Wigren
While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems. One reason for t…
Robust nonlinear set-point control with reinforcement learning
Ruoqi Zhang, Per Mattsson, Torbjörn Wigren
There has recently been an increased interest in reinforcement learning for nonlinear control problems. However standard reinforcement learning algorithms can often struggle even o…
Observer-Feedback-Feedforward Controller Structures in Reinforcement Learning
Ruoqi Zhang, Per Mattson, Torbjörn Wigren
The paper proposes the use of structured neural networks for reinforcement learning based nonlinear adaptive control. The focus is on partially observable systems, with separate ne…