Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning

Mehdi Dadvar; Rashmeet Kaur Nayyar; Siddharth Srivastava

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arXiv · 2210.01955

Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning

Mehdi Dadvar·Rashmeet Kaur Nayyar·Siddharth Srivastava

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Abstract

In many real-world problems, the learning agent needs to learn a problem's abstractions and solution simultaneously. However, most such abstractions need to be designed and refined by hand for different problems and domains of application. This paper presents a novel top-down approach for constructing state abstractions while carrying out reinforcement learning. Starting with state variables and a simulator, it presents a novel domain-independent approach for dynamically computing an abstraction based on the dispersion of Q-values in abstract states as the agent continues acting and learning. Extensive empirical evaluation on multiple domains and problems shows that this approach automatically learns abstractions that are finely-tuned to the problem, yield powerful sample efficiency, and result in the RL agent significantly outperforming existing approaches.

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Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava. 2022-10-04. Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning. https://arxiv.org/abs/2210.01955

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