Learning State Abstractions for Transfer in Continuous Control

Kavosh Asadi; David Abel; Michael L. Littman

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arXiv · 2002.05518

Learning State Abstractions for Transfer in Continuous Control

Kavosh Asadi·David Abel·Michael L. Littman

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Abstract

Can simple algorithms with a good representation solve challenging reinforcement learning problems? In this work, we answer this question in the affirmative, where we take "simple learning algorithm" to be tabular Q-Learning, the "good representations" to be a learned state abstraction, and "challenging problems" to be continuous control tasks. Our main contribution is a learning algorithm that abstracts a continuous state-space into a discrete one. We transfer this learned representation to unseen problems to enable effective learning. We provide theory showing that learned abstractions maintain a bounded value loss, and we report experiments showing that the abstractions empower tabular Q-Learning to learn efficiently in unseen tasks.

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Kavosh Asadi, David Abel, Michael L. Littman. 2020-02-08. Learning State Abstractions for Transfer in Continuous Control. https://arxiv.org/abs/2002.05518

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