Hierarchical Primitive Composition: Simultaneous Activation of Skills with Inconsistent Action Dimensions in Multiple Hierarchies

TL;DR

This paper introduces a hierarchical skill composition framework in deep reinforcement learning that enables simultaneous skill activation across multiple hierarchies with different action spaces, improving modularity and reusability.

Contribution

It proposes a novel algorithm for orchestrating skills with varying action dimensions using multiplicative Gaussian distributions in a recursive hierarchical structure.

Findings

Effective in a 6 DoF pick and place task

Enhances modularity and interpretability of policies

Ablation studies confirm the benefits of the proposed properties

Abstract

Deep reinforcement learning has shown its effectiveness in various applications, providing a promising direction for solving tasks with high complexity. However, naively applying classical RL for learning a complex long-horizon task with a single control policy is inefficient. Thus, policy modularization tackles this problem by learning a set of modules that are mapped to primitives and properly orchestrating them. In this study, we further expand the discussion by incorporating simultaneous activation of the skills and structuring them into multiple hierarchies in a recursive fashion. Moreover, we sought to devise an algorithm that can properly orchestrate the skills with different action spaces via multiplicative Gaussian distributions, which highly increases the reusability. By exploiting the modularity, interpretability can also be achieved by observing the modules that are used in the new task if each of the skills is known. We demonstrate how the proposed scheme can be employed in practice by solving a pick and place task with a 6 DoF manipulator, and examine the effects of each property from ablation studies.