Enabling Option Learning in Sparse Rewards with Hindsight Experience Replay

TL;DR

This paper introduces MOC-2HER, a novel method combining hierarchical reinforcement learning with dual goal relabeling, significantly improving success rates in sparse reward robotic manipulation tasks.

Contribution

It proposes a dual objectives HER extension that enhances hierarchical RL in multi-goal sparse reward environments, especially for object manipulation.

Findings

MOC-2HER achieves up to 90% success rate in robotic tasks.

Standard MOC and MOC-HER achieve less than 11% success.

Dual goal relabeling improves learning efficiency in sparse rewards.

Abstract

Hierarchical Reinforcement Learning (HRL) frameworks like Option-Critic (OC) and Multi-updates Option Critic (MOC) have introduced significant advancements in learning reusable options. However, these methods underperform in multi-goal environments with sparse rewards, where actions must be linked to temporally distant outcomes. To address this limitation, we first propose MOC-HER, which integrates the Hindsight Experience Replay (HER) mechanism into the MOC framework. By relabeling goals from achieved outcomes, MOC-HER can solve sparse reward environments that are intractable for the original MOC. However, this approach is insufficient for object manipulation tasks, where the reward depends on the object reaching the goal rather than on the agent's direct interaction. This makes it extremely difficult for HRL agents to discover how to interact with these objects. To overcome this issue, we introduce Dual Objectives Hindsight Experience Replay (2HER), a novel extension that creates two sets of virtual goals. In addition to relabeling goals based on the object's final state (standard HER), 2HER also generates goals from the agent's effector positions, rewarding the agent for both interacting with the object and completing the task. Experimental results in robotic manipulation environments show that MOC-2HER achieves success rates of up to 90%, compared to less than 11% for both MOC and MOC-HER. These results highlight the effectiveness of our dual objective relabeling strategy in sparse reward, multi-goal tasks.