Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning

• URL: http://arxiv.org/abs/2602.02098v1
• Date: Mon, 02 Feb 2026 13:41:47 GMT
• Title: Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning
• Authors: Yannik Schnitzer, Mathias Jackermeier, Alessandro Abate, David Parker,
• Abstract summary: Multi-task reinforcement learning trains policies that can execute multiple tasks.<n>Existing approaches rarely provide formal performance guarantees.<n>We present an approach for computing high-confidence guarantees on the performance of a multi-task policy on tasks not seen during training.
• Score: 52.91674663354141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task reinforcement learning trains generalist policies that can execute multiple tasks. While recent years have seen significant progress, existing approaches rarely provide formal performance guarantees, which are indispensable when deploying policies in safety-critical settings. We present an approach for computing high-confidence guarantees on the performance of a multi-task policy on tasks not seen during training. Concretely, we introduce a new generalisation bound that composes (i) per-task lower confidence bounds from finitely many rollouts with (ii) task-level generalisation from finitely many sampled tasks, yielding a high-confidence guarantee for new tasks drawn from the same arbitrary and unknown distribution. Across state-of-the-art multi-task RL methods, we show that the guarantees are theoretically sound and informative at realistic sample sizes.

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