8 citations · 8 across the 2 of their papers we have counts for
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cs.AI2025
Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences
Emmanuel Fashae, Michael Burke, Leimin Tian +2
Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limi…
cs.AI2020★ 8 cited
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning
Lingheng Meng, Rob Gorbet, Dana Kulić
Multi-step (also called n-step) methods in reinforcement learning (RL) have been shown to be more efficient than the 1-step method due to faster propagation of the reward signal, b…