# Compositional Transfer in Hierarchical Reinforcement Learning

## TL;DR

This paper introduces RHPO, a hierarchical reinforcement learning algorithm that improves data efficiency and transfer capabilities in robotics by leveraging compositional inductive biases and sharing mechanisms across tasks.

## Contribution

The paper presents RHPO, a novel hierarchical policy optimization method that enhances data efficiency and transfer learning in complex robotics tasks.

## Key findings

- RHPO achieves faster learning in real-world robotics tasks.
- The hierarchical approach enables positive transfer and reduces negative interference.
- Substantial data-efficiency and performance gains are demonstrated in robot stacking experiments.

## Abstract

The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements. We introduce Regularized Hierarchical Policy Optimization (RHPO) to improve data-efficiency for domains with multiple dominant tasks and ultimately reduce required platform time. To this end, we employ compositional inductive biases on multiple levels and corresponding mechanisms for sharing off-policy transition data across low-level controllers and tasks as well as scheduling of tasks. The presented algorithm enables stable and fast learning for complex, real-world domains in the parallel multitask and sequential transfer case. We show that the investigated types of hierarchy enable positive transfer while partially mitigating negative interference and evaluate the benefits of additional incentives for efficient, compositional task solutions in single task domains. Finally, we demonstrate substantial data-efficiency and final performance gains over competitive baselines in a week-long, physical robot stacking experiment.