Sub-policy Adaptation for Hierarchical Reinforcement Learning

Alexander Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel

TL;DR: We propose HiPPO, a stable Hierarchical Reinforcement Learning algorithm that can train several levels of the hierarchy simultaneously, giving good performance both in skill discovery and adaptation.

Abstract: Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Leaving the skills fixed can lead to significant sub-optimality in the transfer setting. In this work, we propose a novel algorithm to discover a set of skills, and continuously adapt them along with the higher level even when training on a new task. Our main contributions are two-fold. First, we derive a new hierarchical policy gradient with an unbiased latent-dependent baseline, and we introduce Hierarchical Proximal Policy Optimization (HiPPO), an on-policy method to efficiently train all levels of the hierarchy jointly. Second, we propose a method of training time-abstractions that improves the robustness of the obtained skills to environment changes. Code and videos are available at sites.google.com/view/hippo-rl.

Keywords: Hierarchical Reinforcement Learning, Transfer, Skill Discovery

Code: https://anonymous.4open.science/r/de105a6d-8f8b-405e-b90a-54ab74adcb17/