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Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters
2019
Neural Information Processing Systems
How does the uncertainty of the value function propagate when performing temporal difference learning? In this paper, we address this question by proposing a Bayesian framework in which we employ approximate posterior distributions to model the uncertainty of the value function and Wasserstein barycenters to propagate it across state-action pairs. Leveraging on these tools, we present an algorithm, Wasserstein Q-Learning (WQL), starting in the tabular case and then, we show how it can be
dblp:conf/nips/MetelliLR19
fatcat:fopzpftyqffy5jhmgej3lr5xgu