Multi-Task Reinforcement Learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems. To this end, sharing representations plays a fundamental role in capturing both unique and common characteristics of the tasks. Tasks may exhibit similarities in terms of skills, objects, or physical properties while leveraging their representations eases the achievement of a universal policy. Nevertheless, the pursuit of learning a shared set of diverse representations is still an open challenge. In this paper, we introduce a novel approach for representation learning in MTRL that encapsulates common structures among the tasks using orthogonal representations to promote diversity. Our method, named Mixture Of Orthogonal Experts (MOORE), leverages a Gram-Schmidt process to shape a shared subspace of representations generated by a mixture of experts. When task-specific information is provided, MOORE generates relevant representations from this shared subspace. We assess the effectiveness of our approach on two MTRL benchmarks, namely MiniGrid and MetaWorld, showing that MOORE surpasses related baselines and establishes a new state-of-the-art result on MetaWorld.

多任务强化学习(MTRL)通过共享表示，来克服代理程序普适性技能泛化的长期问题，本文介绍了在MTRL中学习共享多样性表示的一种新方法，称为MOORE，它通过专家混合生成的表示的共享子空间来促进任务之间的多样性，研究表明MOORE在MiniGrid和MetaWorld两个基准测试中超过了其他相关基线，成为MetaWorld领域的最新技术成果。

混合正交专家的多任务强化学习