ACTRCE: Augmenting Experience via Teacher’s Advice

Yuhuai Wu, Harris Chan, Jamie Kiros, Sanja Fidler, Jimmy Ba

Abstract: Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failure experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, but we also found that little amount of hindsight advice is sufficient for the learning to take off, showing the practical aspect of the method.

Keywords: language goals, task generalization, hindsight experience replays, language grounding

TL;DR: Combine language goal representation with hindsight experience replays.

Data: [VizDoom](https://paperswithcode.com/dataset/vizdoom)