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Unifying Ensemble Methods for Q-learning via Social Choice Theory
[article]
2019
arXiv
pre-print
Ensemble methods have been widely applied in Reinforcement Learning (RL) in order to enhance stability, increase convergence speed, and improve exploration. These methods typically work by employing an aggregation mechanism over actions of different RL algorithms. We show that a variety of these methods can be unified by drawing parallels from committee voting rules in Social Choice Theory. We map the problem of designing an action aggregation mechanism in an ensemble method to a voting problem
arXiv:1902.10646v2
fatcat:7qb6yrhflvcwrk5rookauuveha