<ul data-eligibleForWebStory="false"><li>Multi-task reinforcement learning aims to efficiently learn multiple tasks simultaneously by leveraging shared information.</li><li>A new framework called Cross-Task Policy Guidance (CTPG) is introduced to provide guidance for unmastered tasks by utilizing control policies of proficient tasks.</li><li>CTPG uses guide policies to select behavior policies from various tasks, enhancing training trajectories.</li><li>Empirical evaluations show that integrating CTPG with existing approaches improves performance in manipulation and locomotion benchmarks.</li></ul>

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance

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