75 citations · 76 across the 5 of their papers we have counts for
5 papers
Off-policy Reinforcement Learning with Model-based Exploration Augmentation
Likun Wang, Xiangteng Zhang, Yinuo Wang +5
Exploration is fundamental to reinforcement learning (RL), as it determines how effectively an agent discovers and exploits the underlying structure of its environment to achieve o…
Enhanced DACER Algorithm with High Diffusion Efficiency
Yinuo Wang, Likun Wang, Mining Tan +11
Due to their expressive capacity, diffusion models have shown great promise in offline RL and imitation learning. Diffusion Actor-Critic with Entropy Regulator (DACER) extended thi…
Diffusion Actor-Critic with Entropy Regulator
Yinuo Wang, Likun Wang, Yuxuan Jiang +8
Reinforcement learning (RL) has proven highly effective in addressing complex decision-making and control tasks. However, in most traditional RL algorithms, the policy is typically…
Training Multi-layer Neural Networks on Ising Machine
Xujie Song, Tong Liu, Shengbo Eben Li +3
As a dedicated quantum device, Ising machines could solve large-scale binary optimization problems in milliseconds. There is emerging interest in utilizing Ising machines to train…
Distributional Soft Actor-Critic with Three Refinements
Jingliang Duan, Wenxuan Wang, Liming Xiao +6
Reinforcement learning (RL) has shown remarkable success in solving complex decision-making and control tasks. However, many model-free RL algorithms experience performance degrada…