Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

arXiv:2311.11385v2 Announce Type: replace
Abstract: 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 …

abstract agents arxiv cs.lg experts fundamental reinforcement reinforcement learning role skills tasks terms type unique