Similarity-based Knowledge Transfer for Cross-Domain Reinforcement
  Learning
Sergio A. Serrano Jose Martinez-Carranza L. Enrique Sucar
Transferring knowledge in cross-domain reinforcement learning is a
challenging setting in which learning is accelerated by reusing knowledge from
a task with different observation and/or action space. However, it is often
necessary to carefully select the source of knowledge for the receiving end to
benefit from the transfer process. In this article, we study how to measure the
similarity between cross-domain reinforcement learning tasks to select a source
of knowledge that will improve the performance of the learning agent. We
developed a semi-supervised alignment loss to match different spaces with a set
of encoder-decoders, and use them to measure similarity and transfer policies
across tasks. In comparison to prior works, our method does not require data to
be aligned, paired or collected by expert policies. Experimental results, on a
set of varied Mujoco control tasks, show the robustness of our method in
effectively selecting and transferring knowledge, without the supervision of a
tailored set of source tasks.