reinforcement learning suffers from limitations in real practices primarily
due to the numbers of required interactions with virtual environments. It
results in a challenging problem that we are implausible to obtain an optimal
strategy only with a few attempts for many learning method
本文提出了一种新的基于模型的强化学习算法 MPPVE（Model-based Planning Policy Learning with Multi-step Plan Value Estimation），通过引入多步计划来替换多步行动，采用多步计划价值估计来更新政策，从而更好地利用学习到的模型，实现比现有基于模型的强化学习方法更好的样本效率。