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Sparse reward is one of the most challenging problems in reinforcementlearning (RL). Hindsight Experience Replay (HER) attempts to address this issueby converting a failed experience to a successful one by relabeling the goals.Despite its effectiveness, HER has limited applicability because it lacks acompact and universal goal representation. We present Augmenting experienCe viaTeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique thatextends the HER framework using natural language as the goal representation. Wefirst analyze the differences among goal representation, and show that ACTRCEcan efficiently solve difficult reinforcement learning problems in challenging3D navigation tasks, whereas HER with non-language goal representation failedto learn. We also show that with language goal representations, the agent cangeneralize to unseen instructions, and even generalize to instructions withunseen lexicons. We further demonstrate it is crucial to use hindsight adviceto solve challenging tasks, and even small amount of advice is sufficient forthe agent to achieve good performance.

ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning