221 citations · 246 across the 11 of their papers we have counts for
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cs.LG2023
Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs
Qingyang Zhang, Yiming Yang, Jingqing Ruan +3
Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) is a promising paradigm to address the exploration-exploitation dilemma in reinforcement learning. It decomposes the so…
cs.LG2023★ 6 cited
Policy Representation via Diffusion Probability Model for Reinforcement Learning
Long Yang, Zhixiong Huang, Fenghao Lei +6
Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of expl…
cs.LG2022★ 1 cited
FLOWGEN: Fast and slow graph generation
Aman Madaan, Yiming Yang
Machine learning systems typically apply the same model to both easy and tough cases. This is in stark contrast with humans, who tend to evoke either fast (instinctive) or slow (an…