5 citations · 18 across the 16 of their papers we have counts for
7 papers · 1 filter
Dichotomous Diffusion Policy Optimization
Ruiming Liang, Yinan Zheng, Kexin Zheng +9
Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inf…
Policy Bifurcation in Safe Reinforcement Learning
Wenjun Zou, Yao Lyu, Jie Li +7
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, wh…
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Yinan Zheng, Jianxiong Li, Dongjie Yu +4
Safe offline RL is a promising way to bypass risky online interactions towards safe policy learning. Most existing methods only enforce soft constraints, i.e., constraining safety…
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Jianxiong Li, Xiao Hu, Haoran Xu +3
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance. H…
Feasible Policy Iteration for Safe Reinforcement Learning
Yujie Yang, Zhilong Zheng, Shengbo Eben Li +4
Safety is the priority concern when applying reinforcement learning (RL) algorithms to real-world control problems. While policy iteration provides a fundamental algorithm for stan…
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
Jianxiong Li, Xiao Hu, Haoran Xu +4
Reward function is essential in reinforcement learning (RL), serving as the guiding signal to incentivize agents to solve given tasks, however, is also notoriously difficult to des…
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
Jianxiong Li, Xianyuan Zhan, Haoran Xu +3
In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, exi…