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

Jeonghye Kim; Woojun Kim; Youngchul Sung

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

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arxiv 2310.03342 v2 pith:NYLSU57A submitted 2023-10-05 cs.LG

Woojun Kim , Jeonghye Kim , Youngchul Sung This is my paper · ORCID

classification cs.LG

keywords explorationframeworklearningproposedintegratereinforcementstrategiesadaptively

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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.

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Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning
cs.LG 2024-12 reject novelty 4.0 of 10

Hyper makes curiosity-driven RL less sensitive to the exploration bonus weight by separating repositioning from exploration, with a claimed polynomial sample-complexity bound.

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