On Effective Scheduling of Model-based Reinforcement Learning. (arXiv:2111.08550v3 [cs.LG] UPDATED)

Model-based reinforcement learning has attracted wide attention due to its
superior sample efficiency. Despite its impressive success so far, it is still
unclear how to appropriately schedule the important hyperparameters to achieve
adequate performance, such as the real data ratio for policy optimization in
Dyna-style model-based algorithms. In this paper, we first theoretically
analyze the role of real data in policy training, which suggests that gradually
increasing the ratio of real data yields better performance. Inspired by the
analysis, we …

arxiv learning lg reinforcement reinforcement learning scheduling