Discovering state-of-the-art reinforcement learning algorithms | Nature

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature volume  648,  pages 312–319 (2025)Cite this article 39k Accesses 1 Citations 221 Altmetric Metrics details Humans and other animals use powerful reinforcement learning (RL) mechanisms that have been discovered by evolution over many generations of trial and error. By contrast, artificial agents typically learn using handcrafted learning rules. Despite decades of interest, the goal of autonomously discovering powerful RL algorithms has proven to be elusive1,2,3,4,5,6. Here we show that it is possible for machines to discover a state-of-the-art RL rule that outperforms manually designed rules. This was

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• Lydia Nottingham