  Due to recent breakthroughs, reinforcement learning (RL) has demonstrated
impressive performance in challenging sequential decision-making problems.
However, an open question is how to make RL cope with partial observability
which is prevalent in many real-world problems. Contrary to contemporary RL
approaches, which focus mostly on improved memory representations or strong
assumptions about the type of partial observability, we propose a simple but
efficient approach that can be applied together with a wide variety of RL
methods. Our main insight is that smoothly transitioning from full
observability to partial observability during the training process yields a
high performance policy. The approach, called partially observable guided
reinforcement learning (PO-GRL), allows to utilize full state information
during policy optimization without compromising the optimality of the final
policy. A comprehensive evaluation in discrete partially observableMarkov
decision process (POMDP) benchmark problems and continuous partially observable
MuJoCo and OpenAI gym tasks shows that PO-GRL improves performance. Finally, we
demonstrate PO-GRL in the ball-in-the-cup task on a real Barrett WAM robot
under partial observability.
