343 citations · 615 across the 4 of their papers we have counts for
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cs.LG2018
Relational Deep Reinforcement Learning
Vinicius Zambaldi, David Raposo, Adam Santoro +13
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through st…
cs.CV2018
Synthesizing Programs for Images using Reinforced Adversarial Learning
Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin +2
Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumabl…