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

Woojun Kim; Jeonghye Kim; Youngchul Sung

ICML 2023poster6 citations

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

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 architecture. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy to realize an effective 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.

BibTeX

@inproceedings{icml2023_lessonlearningto,
  title = {LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework},
  author = {Woojun Kim and Jeonghye Kim and Youngchul Sung},
  booktitle = {ICML 2023},
  year = {2023}
}