most citedEnhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization

2 citations · 5 across the 4 of their papers we have counts for

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cs.LG2025★ 1 cited

AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors

Ruoxuan Feng, Jiangyu Hu, Wenke Xia +5

Visuo-tactile sensors aim to emulate human tactile perception, enabling robots to precisely understand and manipulate objects. Over time, numerous meticulously designed visuo-tacti…

cs.LG2024★ 2 cited

Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization

Tianci Gao, Konstantin A. Neusypin, Dmitry D. Dmitriev +2

Proximal Policy Optimization (PPO) is widely used in continuous control due to its robustness and stable training, yet it remains sample-inefficient in tasks with expensive interac…