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⇱ Extended Data Table 2 Comparison of games scores obtained by DQN agents with methods from the literature<sup><a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 12" title="Bellemare, M. G., Naddaf, Y., Veness, J. & Bowling, M. The arcade learning environment: An evaluation platform for general agents. J. Artif. Intell. Res. 47, 253–279 (2013)" href="/articles/nature14236#ref-CR12" id="ref-link-section-d618056426e2257">12</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 15" title="Bellemare, M. G., Veness, J. & Bowling, M. Investigating contingency awareness using Atari 2600 games. Proc. Conf. AAAI. Artif. Intell. 864–871 (2012)" href="/articles/nature14236#ref-CR15" id="ref-link-section-d618056426e2260">15</a></sup> and a professional human games tester | Nature


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  1. Best Linear Learner is the best result obtained by a linear function approximator on different types of hand designed features12. Contingency (SARSA) agent figures are the results obtained in ref. 15. Note the figures in the last column indicate the performance of DQN relative to the human games tester, expressed as a percentage, that is, 100 × (DQN score − random play score)/(human score − random play score).
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