37 citations · 116 across the 12 of their papers we have counts for
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cs.LG2018★ 2 cited
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
Michael Schaarschmidt, Sven Mika, Kai Fricke +1
Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communic…
cs.LG2018★ 37 cited
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
Michael Schaarschmidt, Alexander Kuhnle, Ben Ellis +3
Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent…