Improving Offline Reinforcement Learning with Inaccurate Simulators
Yiwen Hou,Haoyuan Sun,Jinming Ma,Feng Wu,Yiwen Hou,Haoyuan Sun,Jinming Ma,Feng Wu
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 inacc...
