LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

Woojun Kim; Jeonghye Kim; Youngchul Sung

LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

Machine Learning 2024-09-10 v2

Authors: Woojun Kim , Jeonghye Kim , Youngchul Sung

Abstract

In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy over time to realize a relevant exploration-exploitation trade-off for each given task. The effectiveness of the proposed exploration framework is demonstrated by various experiments in the MiniGrid and Atari environments.

Keywords

reinforcement learning hierarchical reinforcement learning reinforcement learning from human feedback

Cite

@article{arxiv.2310.03342,
  title  = {LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework},
  author = {Woojun Kim and Jeonghye Kim and Youngchul Sung},
  journal= {arXiv preprint arXiv:2310.03342},
  year   = {2024}
}

Comments

Accepted to ICML2023. Our code is available at https://github.com/beanie00/LESSON

LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

Abstract

Keywords

Cite

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