1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchTricia Mayerhttps://www.microsoft.com/en-us/research/people/tmayer/Deep Reinforcement Learning that Matters - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="iKiGTddHcj"><a href="https://www.microsoft.com/en-us/research/publication/deep-reinforcement-learning-matters/">Deep Reinforcement Learning that Matters</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/deep-reinforcement-learning-matters/embed/#?secret=iKiGTddHcj" width="600" height="338" title="“Deep Reinforcement Learning that Matters” — Microsoft Research" data-secret="iKiGTddHcj" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark […]