A Reliable Representation with Bidirectional Transition Model for Visual
  Reinforcement Learning Generalization
Xiaobo Hu Youfang Lin Yue Liu Jinwen Wang Shuo Wang Hehe Fan Kai Lv
Visual reinforcement learning has proven effective in solving control tasks
with high-dimensional observations. However, extracting reliable and
generalizable representations from vision-based observations remains a central
challenge. Inspired by the human thought process, when the representation
extracted from the observation can predict the future and trace history, the
representation is reliable and accurate in comprehending the environment. Based
on this concept, we introduce a Bidirectional Transition (BiT) model, which
leverages the ability to bidirectionally predict environmental transitions both
forward and backward to extract reliable representations. Our model
demonstrates competitive generalization performance and sample efficiency on
two settings of the DeepMind Control suite. Additionally, we utilize robotic
manipulation and CARLA simulators to demonstrate the wide applicability of our
method.