34 citations · 135 across the 25 of their papers we have counts for
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cs.AI2020★ 6 cited
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt +9
Recent work in deep reinforcement learning (RL) has produced algorithms capable of mastering challenging games such as Go, chess, or shogi. In these works the RL agent directly obs…
cs.AI2017★ 27 cited
QMDP-Net: Deep Learning for Planning under Partial Observability
Peter Karkus, David Hsu, Wee Sun Lee
This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-bas…