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Related Experiment Video

Updated: Nov 1, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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End-to-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery.

Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

IEEE Transactions on Neural Networks and Learning Systems

|June 22, 2021

Summary

Hierarchical reinforcement learning (HRL) agents can now discover subgoals more efficiently. The new LIDOSS method reduces search space, improving goal achievement in complex tasks.

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Area of Science:

Artificial Intelligence
Robotics
Machine Learning

Background:

Hierarchical reinforcement learning (HRL) decomposes long-horizon tasks into subgoals.
Holistic HRL requires agents to autonomously discover subgoals and learn policies.
End-to-end HRL methods search continuous subgoal spaces, which can be challenging with large spaces.

Purpose of the Study:

To propose an end-to-end HRL method that improves subgoal discovery.
To reduce the search space for the higher-level policy in HRL.
To enhance goal achievement rates in continuous control tasks.

Main Methods:

Introduced LIDOSS (Learning Integrated Discovery Of Salient Subgoals), an end-to-end HRL method.
Integrated a subgoal discovery heuristic to focus on probable subgoals.
Evaluated LIDOSS on continuous control tasks in the MuJoCo domain.

Main Results:

LIDOSS demonstrated improved goal achievement rates compared to state-of-the-art HAC.
The method effectively reduced the search space for the higher-level policy.
LIDOSS showed superior performance in most evaluated tasks.

Conclusions:

LIDOSS offers a more efficient approach to subgoal discovery in end-to-end HRL.
The integrated heuristic significantly enhances performance in complex goal-reaching tasks.
This method advances the capabilities of autonomous agents in challenging environments.