Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning

• URL: http://arxiv.org/abs/2210.01955v1
• Date: Tue, 4 Oct 2022 23:05:43 GMT
• Title: Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning
• Authors: Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava
• Abstract summary: In many real-world problems, the learning agent needs to learn a problem's abstractions and solution simultaneously. This paper presents a novel top-down approach for constructing state abstractions while carrying out reinforcement learning.
• Score: 22.25237742815589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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