Learning Reward Structure with Subtasks in Reinforcement Learning

Abstract

Improving sample efficiency of Reinforcement Learning (RL) in sparse-reward environments poses a significant challenge. In scenarios where the reward structure is complex, accurate action evaluation often relies heavily on precise information about past achieved subtasks and their order. Previous approaches have often failed or proved inefficient in constructing and leveraging such intricate reward structures. In this work, we propose an RL algorithm that can automatically structure the reward function for sample efficiency, given a set of labels that signify subtasks. Given such minimal knowledge about the task, we train a high-level policy that selects optimal subtasks in each state together with a low-level policy that efficiently learns to complete each sub-task. We evaluate our algorithm in a variety of sparse-reward environments. The experiment results show that our method significantly outperforms the state-of-art baselines as the difficulty of the task increases.