Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making

• URL: http://arxiv.org/abs/2111.11212v1
• Date: Thu, 18 Nov 2021 20:17:07 GMT
• Title: Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
• Authors: Alex Kearney, Anna Koop, Johannes G\"unther, Patrick M. Pilarski
• Abstract summary: We focus on predictions expressed as General Value Functions: temporally extended estimates of the accumulation of a future signal. One challenge is determining from the infinitely many predictions that the agent could possibly make which might support decision-making. By learning, rather than manually specifying these predictions, we enable the agent to identify useful predictions in a self-supervised manner.
• Score: 1.384055225262046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations. In this manuscript we focus on predictions expressed as General Value Functions: temporally extended estimates of the accumulation of a future signal. One challenge is determining from the infinitely many predictions that the agent could possibly make which might support decision-making. In this work, we contribute a meta-gradient descent method by which an agent can directly specify what predictions it learns, independent of designer instruction. To that end, we introduce a partially observable domain suited to this investigation. We then demonstrate that through interaction with the environment an agent can independently select predictions that resolve the partial-observability, resulting in performance similar to expertly chosen value functions. By learning, rather than manually specifying these predictions, we enable the agent to identify useful predictions in a self-supervised manner, taking a step towards truly autonomous systems.

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