Federated reinforcement learning for robot motion planning with zero-shot generalization

arXiv:2403.13245v1 Announce Type: cross
Abstract: This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central server, i.e., the Cloud, without sharing their raw data. In each iteration, each learner uploads its local control policy and the corresponding estimated …

abstract arxiv collaborative collection control cs.ai cs.dc cs.lg cs.ro cs.sy data data collection eess.sy environments framework motion planning paper planning policy reinforcement reinforcement learning robot type zero-shot