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Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
2020
Neural Information Processing Systems
Recent Reinforcement Learning (RL) algorithms making use of Kullback-Leibler (KL) regularization as a core component have shown outstanding performance. Yet, only little is understood theoretically about why KL regularization helps, so far. We study KL regularization within an approximate value iteration scheme and show that it implicitly averages q-values. Leveraging this insight, we provide a very strong performance bound, the very first to combine two desirable aspects: a linear dependency
dblp:conf/nips/VieillardKSPMG20
fatcat:hs3wz355qjedlnokjxys7nosli