基於模型的強化式學習架構預測環境的走向,大多數基於模型的強化式學習架構選擇遞迴網路作為推斷潛在走向的環境模型,基於模型的強化式學習架構也承襲了遞迴網絡的問題。在本文中,我們分析了先前方法中的遞迴網路問題,並提出了解決這些問題的方法。在 DeepMind 控制上的實驗表明,所提出的方法比先前方法實現了更好的平均表現。

Model-based Reinforcement Learning (MBRL) consumes the high-dimensional pixel observations as inputs, whereas the latent dynamics model can learn the image inputs into latent state spaces. Most MBRL choose RNN as the world model for inferring latent dynamics. The use of recurrent networks inherits the RNN issue to MBRL. In this paper, we analyze RNN issues in Dreamer and propose a method to resolve the issues. We show our proposed method on the DeepMind Control suite and achieve better overall performance than Dreamer.

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