Minimum Description Length Control

Ted Moskovitz; Ta-Chu Kao; Maneesh Sahani; Matthew M. Botvinick

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arXiv · 2207.08258

Minimum Description Length Control

Ted Moskovitz·Ta-Chu Kao·Maneesh Sahani·Matthew M. Botvinick

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Abstract

We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced and then distills it into a simpler representation which facilitates faster convergence and generalization to new tasks. In doing so, MDL-C naturally balances adaptation to each task with epistemic uncertainty about the task distribution. We motivate MDL-C via formal connections between the MDL principle and Bayesian inference, derive theoretical performance guarantees, and demonstrate MDL-C's empirical effectiveness on both discrete and high-dimensional continuous control tasks.

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Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matthew M. Botvinick. 2022-07-17. Minimum Description Length Control. https://arxiv.org/abs/2207.08258

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