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In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources ofdoi:10.7717/peerj-cs.575 pmid:34141896 pmcid:PMC8176548 fatcat:ptng2mzyfzcarkchupz4ii7che