  While the rapid progress of deep learning fuels end-to-end reinforcement
learning (RL), direct application, especially in high-dimensional space like
robotic scenarios still suffers from low sample efficiency. Therefore State
Representation Learning (SRL) is proposed to specifically learn to encode
task-relevant features from complex sensory data into low-dimensional states.
However, the pervasive implementation of SRL is usually conducted by a
decoupling strategy in which the observation-state mapping is learned
separately, which is prone to over-fit. To handle such problem, we summarize
the state-of-the-art (SOTA) SRL sub-tasks in previous works and present a new
algorithm called Policy Optimization via Abstract Representation which
integrates SRL into the policy optimization phase. Firstly, We engage RL loss
to assist in updating SRL model so that the states can evolve to meet the
demand of RL and maintain a good physical interpretation. Secondly, we
introduce a dynamic loss weighting mechanism so that both models can
efficiently adapt to each other. Thirdly, we introduce a new SRL prior called
domain resemblance to leverage expert demonstration to improve SRL
interpretations. Finally, we provide a real-time access of state graph to
monitor the course of learning. Experiments indicate that POAR significantly
outperforms SOTA RL algorithms and decoupling SRL strategies in terms of sample
efficiency and final rewards. We empirically verify POAR to efficiently handle
tasks in high dimensions and facilitate training real-life robots directly from
scratch.
