Abstract Multi-task learning is important in reinforcement learning where simultaneously training across different tasks allows for leveraging shared information among them, typically leading to better performance than single-task learning. While joint training of multiple tasks permits parameter sharing between tasks, the optimization challenge becomes crucial—identifying which parameters should be reused and managing potential gradient conflicts arising from different tasks. To tackle this issue, instead of unif…