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For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Code provided as supplemental. If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? A.1 Implementation, Hyperparameters and Evaluation Details The implementation of our main agent, Tandem DQN, is based on the Double-DQN [van Hasselt et al., 2016] agent provided in the DQN Zoo open-source agent collection [Quan and Ostrovski, 2020]. Figure 12: Tandem DQN: Active vs. passive performance on four selected Classic Control domains.