Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning

Gavin Rens

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Paper

Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning

Topics: Cognitive Robotics; Task Planning and Execution

In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, 507-514, 2025 , Porto, Portugal

Author: Gavin Rens

Affiliation: Computer Science Division, Stellenbosch University, Stellenbosch, South Africa

Keyword(s): Reinforcement Learning, Monte Carlo Tree Search, Hierarchical, Goal-Conditioned, Multi-Goal.

Abstract: Humanoid robots must master numerous tasks with sparse rewards, posing a challenge for reinforcement learning (RL). We propose a method combining RL and automated planning to address this. Our approach uses short goal-conditioned policies (GCPs) organized hierarchically, with Monte Carlo Tree Search (MCTS) planning using high-level actions (HLAs). Instead of primitive actions, the planning process generates HLAs. A single plan-tree, maintained during the agent’s lifetime, holds knowledge about goal achievement. This hierarchy enhances sample efficiency and speeds up reasoning by reusing HLAs and anticipating future actions. Our Hierarchical Goal-Conditioned Policy Planning (HGCPP) framework uniquely integrates GCPs, MCTS, and hierarchical RL, potentially improving exploration and planning in complex tasks.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Rens, G. (2025). Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5; ISSN 2184-433X, SciTePress, pages 507-514. DOI: 10.5220/0013238900003890

@conference{icaart25,
author={Gavin Rens},
title={Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={507-514},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013238900003890},
isbn={978-989-758-737-5},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning
SN - 978-989-758-737-5
IS - 2184-433X
AU - Rens, G.
PY - 2025
SP - 507
EP - 514
DO - 10.5220/0013238900003890
PB - SciTePress