  Recent advances in deep reinforcement learning (deep RL) enable researchers
to solve challenging control problems, from simulated environments to
real-world robotic tasks. However, deep RL algorithms are known to be sensitive
to the problem formulation, including observation spaces, action spaces, and
reward functions. There exist numerous choices for observation spaces but they
are often designed solely based on prior knowledge due to the lack of
established principles. In this work, we conduct benchmark experiments to
verify common design choices for observation spaces, such as Cartesian
transformation, binary contact flags, a short history, or global positions.
Then we propose a search algorithm to find the optimal observation spaces,
which examines various candidate observation spaces and removes unnecessary
observation channels with a Dropout-Permutation test. We demonstrate that our
algorithm significantly improves learning speed compared to manually designed
observation spaces. We also analyze the proposed algorithm by evaluating
different hyperparameters.
