# Hierarchical reinforcement learning for efficient exploration and transfer

arXiv: https://arxiv.org/abs/2011.06335 · HTML: https://mlsanity.com/paper/2011.06335/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Disentangling Transfer in Continual Reinforcement Learning](https://mlsanity.com/paper/2209.13900/) (2022)
- [Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics](https://mlsanity.com/paper/2211.02231/) (2022)
- [Learning Robust Real-Time Cultural Transmission without Human Data](https://mlsanity.com/paper/2203.00715/) (2022)
- [Efficient search of active inference policy spaces using k-means](https://mlsanity.com/paper/2209.02550/) (2022)
- [Toward Autonomous UI Exploration: The UIExplorer Benchmark](https://mlsanity.com/paper/2506.17779/) (2025)

## Abstract (arXiv)

Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time. Hierarchical reinforcement learning can facilitate exploration by reducing the number of decisions necessary before obtaining a reward. In this paper, we present a novel hierarchical reinforcement learning framework based on the compression of an invariant state space that is common to a range of tasks. The algorithm introduces subtasks which consist of moving between the state partitions induced by the compression. Results indicate that the algorithm can successfully solve complex sparse-reward domains, and transfer knowledge to solve new, previously unseen tasks more quickly.