Efficient Multi-Task Reinforcement Learning via Task-Specific Action Correction

arXiv:2404.05950v1 Announce Type: new
Abstract: Multi-task reinforcement learning (MTRL) demonstrate potential for enhancing the generalization of a robot, enabling it to perform multiple tasks concurrently. However, the performance of MTRL may still be susceptible to conflicts between tasks and negative interference. To facilitate efficient MTRL, we propose Task-Specific Action Correction (TSAC), a general and complementary approach designed for simultaneous learning of multiple tasks. TSAC decomposes policy learning into two separate policies: a shared policy (SP) and an action correction policy …

abstract arxiv cs.ai cs.lg cs.ro enabling however interference multiple negative performance reinforcement reinforcement learning robot tasks type via