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

WOOJUN KIM · Jeonghye Kim · Youngchul Sung

Exhibit Hall 1 #725
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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.

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