1.8k citations · 4.8k across the 37 of their papers we have counts for
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Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, i…
Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel +3
In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as co…
Deep Reinforcement Learning with Double Q-learning
Hado van Hasselt, Arthur Guez, David Silver
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common,…
Learning to Predict Independent of Span
Hado van Hasselt, Richard S. Sutton
We consider how to learn multi-step predictions efficiently. Conventional algorithms wait until observing actual outcomes before performing the computations to update their predict…