  The purpose of offline multi-task reinforcement learning (MTRL) is to develop
a unified policy applicable to diverse tasks without the need for online
environmental interaction. Recent advancements approach this through sequence
modeling, leveraging the Transformer architecture's scalability and the
benefits of parameter sharing to exploit task similarities. However, variations
in task content and complexity pose significant challenges in policy
formulation, necessitating judicious parameter sharing and management of
conflicting gradients for optimal policy performance. In this work, we
introduce the Harmony Multi-Task Decision Transformer (HarmoDT), a novel
solution designed to identify an optimal harmony subspace of parameters for
each task. We approach this as a bi-level optimization problem, employing a
meta-learning framework that leverages gradient-based techniques. The upper
level of this framework is dedicated to learning a task-specific mask that
delineates the harmony subspace, while the inner level focuses on updating
parameters to enhance the overall performance of the unified policy. Empirical
evaluations on a series of benchmarks demonstrate the superiority of HarmoDT,
verifying the effectiveness of our approach.
