  Multi-task reinforcement learning (RL) aims to simultaneously learn policies
for solving many tasks. Several prior works have found that relabeling past
experience with different reward functions can improve sample efficiency.
Relabeling methods typically ask: if, in hindsight, we assume that our
experience was optimal for some task, for what task was it optimal? In this
paper, we show that hindsight relabeling is inverse RL, an observation that
suggests that we can use inverse RL in tandem for RL algorithms to efficiently
solve many tasks. We use this idea to generalize goal-relabeling techniques
from prior work to arbitrary classes of tasks. Our experiments confirm that
relabeling data using inverse RL accelerates learning in general multi-task
settings, including goal-reaching, domains with discrete sets of rewards, and
those with linear reward functions.
