  Current reinforcement learning (RL) methods can successfully learn single
tasks but often generalize poorly to modest perturbations in task domain or
training procedure. In this work, we present a decoupled learning strategy for
RL that creates a shared representation space where knowledge can be robustly
transferred. We separate learning the task representation, the forward
dynamics, the inverse dynamics and the reward function of the domain, and show
that this decoupling improves performance within the task, transfers well to
changes in dynamics and reward, and can be effectively used for online
planning. Empirical results show good performance in both continuous and
discrete RL domains.
