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2023 ICML ICML 2023

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

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.

🌉 Interdisciplinary Bridge — Machine Learning and Reinforcement Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Woojun Kim , Jeonghye Kim , Youngchul Sung

Topics

Reinforcement Learning > Methods > Deep RL Reinforcement Learning > Methods > Policy Learning Machine Learning > Learning Paradigms > Meta-Learning Machine Learning > Learning Types > Reinforcement Learning

Keywords

reinforcement learning exploration strategy deep rl exploration-exploitation trade-off option framework option-critic architecture
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