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学习可解释的任务相关状态表示,用于无模型的深度强化学习学习

Tingting Zhao¹, Guixi Li², Tuo Zhao²

¹College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China; RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan.

Neural networks : the official journal of the International Neural Network Society

|September 25, 2024

概括

本研究介绍了无模型深度强化学习的可解释任务相关状态表示 (ETrSR). ETrSR提高了学习效率,并且提供了可解释的状态,而不需要过渡模型.

科学领域:

人工智能的人工智能
机器学习机器学习
机器人技术机器人技术机器人技术

背景情况:

国家表示显著提高了深度强化学习 (DRL) 的速度和数据效率,特别是在视觉任务中.
与任务相关的状态表示可以通过专注于相关特征和过分心来进一步提高性能.
对于任务相关表示的当前方法通常依赖于基于模型的DRL,这需要学习一个过渡函数,这带来了潜在的不准确性的挑战.

研究的目的:

提出一种新的,直接的,强大的方法,用于在无模型的DRL中解释任务相关状态表示 (ETrSR).
为了避免与学习过渡模型相关的复杂性和潜在的绩效下降.
通过可解释的状态表示来提高对DRL决策过程的理解.

主要方法:

从使用β变量自编码器 (β-VAE) 的状态中解脱特征.
使用奖励预测模型来指导功能与特定任务的相关性.
解码与任务相关的特征以产生可解释的状态,绕过过渡模型的需求.

主要成果:

拟议的ETRSR方法在CarRacing环境和DeepMind控制套件 (DMC) 任务上得到了验证.
证明了出色的性能,即使在具有显著分心的环境中.
提供可解释性,提高对代理人的决策过程的理解.

结论:

在没有模型的DRL中,ETRSR提供了一种有效的方法来生成与任务相关的和可解释的状态表示.
该方法提高了学习效率和稳定性,而不需要过渡模型.
在复杂的DRL环境中,ETRSR促进了更好的解释性和性能.

关键词:

自动编码器自动编码器深度强化学习的学习.可以解释的可解释性.没有模特的自由模式.国家代表学习学习学习.

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