3 papers
cs.LG2019
Wield: Systematic Reinforcement Learning With Progressive Randomization
Michael Schaarschmidt, Kai Fricke, Eiko Yoneki
Reinforcement learning frameworks have introduced abstractions to implement and execute algorithms at scale. They assume standardized simulator interfaces but are not concerned wit…
cs.LG2018
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
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…