Improving Offline Reinforcement Learning with Inaccurate Simulators

arXiv:2405.04307v1 Announce Type: cross
Abstract: Offline reinforcement learning (RL) provides a promising approach to avoid costly online interaction with the real environment. However, the performance of offline RL highly depends on the quality of the datasets, which may cause extrapolation error in the learning process. In many robotic applications, an inaccurate simulator is often available. However, the data directly collected from the inaccurate simulator cannot be directly used in offline RL due to the well-known exploration-exploitation dilemma and the dynamic …

abstract applications arxiv cs.ai cs.lg cs.ro datasets environment error however improving offline performance process quality reinforcement reinforcement learning robotic simulator type