4 papers · 1 filter
Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning
Zhaoyu Zhu, Rui Gao, Shuang Li
Wasserstein policy gradient (WPG) is a policy optimization method for reinforcement learning (RL) that exploits the optimal-transport geometry of action distributions. For the entr…
C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs
Rui Gao, Youngseung Jeon, Swastik Roy +2
Large language models (LLMs) show promise for molecular optimization, but aligning them with selective and competing drug-design constraints remains challenging. We propose C-Moral…
Wasserstein Proximal Policy Gradient
Zhaoyu Zhu, Shuhan Zhang, Rui Gao +1
We study policy gradient methods for continuous-action, entropy-regularized reinforcement learning through the lens of Wasserstein geometry. Starting from a Wasserstein proximal up…
DeepHalo: A Neural Choice Model with Controllable Context Effects
Shuhan Zhang, Zhi Wang, Rui Gao +1
Modeling human decision-making is central to applications such as recommendation, preference learning, and human-AI alignment. While many classic models assume context-independent…