  Applying reinforcement learning (RL) to sparse reward domains is notoriously
challenging due to insufficient guiding signals. Common RL techniques for
addressing such domains include (1) learning from demonstrations and (2)
curriculum learning. While these two approaches have been studied in detail,
they have rarely been considered together. This paper aims to do so by
introducing a principled task phasing approach that uses demonstrations to
automatically generate a curriculum sequence. Using inverse RL from
(suboptimal) demonstrations we define a simple initial task. Our task phasing
approach then provides a framework to gradually increase the complexity of the
task all the way to the target task, while retuning the RL agent in each
phasing iteration. Two approaches for phasing are considered: (1) gradually
increasing the proportion of time steps an RL agent is in control, and (2)
phasing out a guiding informative reward function. We present conditions that
guarantee the convergence of these approaches to an optimal policy.
Experimental results on 3 sparse reward domains demonstrate that our task
phasing approaches outperform state-of-the-art approaches with respect to
asymptotic performance.
