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.

通过开发半监督对齐损失来匹配不同空间的一组编码器 - 解码器，本研究研究了如何衡量跨领域强化学习任务之间的相似性，以选择能够提高学习代理性能的知识源。实验结果表明，在各种 Mujoco 控制任务中，我们的方法能够有效地选择和传递知识，而无需与专家策略相匹配、配对或收集数据的监督。