6 papers · 1 filter
GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios
Ke Hu, Shutong Ding, Panxin Tao +2
Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous-control tasks. Among them,…
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
Ziyuan Zhu, Keyu Hu, Zhifei Chen +10
Reconstructing continuous physical fields from sparse measurements is a central inverse problem, but data-driven generative models can produce states that violate governing dynamic…
Distributional Reinforcement Learning with Diffusion Bridge Critics
Shutong Ding, Yimiao Zhou, Ke Hu +5
Recent advances in diffusion-based reinforcement learning (RL) methods have demonstrated promising results in a wide range of continuous control tasks. However, existing works in t…
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning
Shutong Ding, Ke Hu, Shan Zhong +5
Recent advances in reinforcement learning (RL) have demonstrated the powerful exploration capabilities and multimodality of generative diffusion-based policies. While substantial p…
Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement
Shutong Ding, Yimiao Zhou, Ke Hu +4
Recent advances in diffusion models show promising potential to accelerate nonconvex problem solving by leveraging their multimodality. However, most existing diffusion-based optim…
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization
Shutong Ding, Ke Hu, Zhenhao Zhang +5
Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality. It has been verified that utilizing diffusio…