  In this paper, we present a technique that improves the process of training
an agent (using RL) for instruction following. We develop a training curriculum
that uses a nominal number of expert demonstrations and trains the agent in a
manner that draws parallels from one of the ways in which humans learn to
perform complex tasks, i.e by starting from the goal and working backwards. We
test our method on the BabyAI platform and show an improvement in sample
efficiency for some of its tasks compared to a PPO (proximal policy
optimization) baseline.
