45 citations · 133 across the 21 of their papers we have counts for
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cs.LG2019
Planning with Abstract Learned Models While Learning Transferable Subtasks
John Winder, Stephanie Milani, Matthew Landen +5
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple…
cs.LG2019★ 45 cited
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Cayden Codel, Katja Hofmann +9
Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinf…