PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene
  Rearrangement Planning
  Scene Rearrangement Planning (SRP) is an interior task proposed recently. The
previous work defines the action space of this task with handcrafted
coarse-grained actions that are inflexible to be used for transforming scene
arrangement and intractable to be deployed in practice. Additionally, this new
task lacks realistic indoor scene rearrangement data to feed popular
data-hungry learning approaches and meet the needs of quantitative evaluation.
To address these problems, we propose a fine-grained action definition for SRP
and introduce a large-scale scene rearrangement dataset. We also propose a
novel learning paradigm to efficiently train an agent through self-playing,
without any prior knowledge. The agent trained via our paradigm achieves
superior performance on the introduced dataset compared to the baseline agents.
We provide a detailed analysis of the design of our approach in our
experiments.
