Efficient Multi-Task and Transfer Reinforcement Learning with
  Parameter-Compositional Framework
Lingfeng Sun Haichao Zhang Wei Xu Masayoshi Tomizuka
In this work, we investigate the potential of improving multi-task training
and also leveraging it for transferring in the reinforcement learning setting.
We identify several challenges towards this goal and propose a transferring
approach with a parameter-compositional formulation. We investigate ways to
improve the training of multi-task reinforcement learning which serves as the
foundation for transferring. Then we conduct a number of transferring
experiments on various manipulation tasks. Experimental results demonstrate
that the proposed approach can have improved performance in the multi-task
training stage, and further show effective transferring in terms of both sample
efficiency and performance.