An article published on October 24, 2025 presents DiscoRL, a method using meta-learning across agents and environments to discover reinforcement learning update rules.
Published on October 24, 2025, the article describes a meta-learning method across agents and environments for discovering reinforcement learning (RL) update rules. The resulting rule, called DiscoRL, reports state-of-the-art results on benchmarks that include Atari and ProcGen.
The article highlights the possibility of assessing whether learned rules transfer between environments without environment-specific tuning. To check the scope, results, and test conditions, consult the original article and verify its benchmark details and comparisons; the available summary provides no metrics or experimental configurations.