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

2 citations · 2 across the 1 of their papers we have counts for

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1 paper

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…