Resistance to shock analysis of Deep Reinforcement Learning

被引：0

作者：

Pchelintsev, Ilya ^{[1
]}

Lukianchenko, Petr ^{[1
]}

机构：

[1] Higher Sch Econ, Fac Comp Sci, Moscow, Russia

来源：

2024 ZOOMING INNOVATION IN CONSUMER TECHNOLOGIES CONFERENCE, ZINC 2024 | 2024年

关键词：

Deep Reinforcement Learning; Shocks; Deep QNetwork; Airport Surface Planning;

D O I：

10.1109/ZINC61849.2024.10579381

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper considers the process of adapting of the trained deep reinforcement learning (DRL) models to the unpredicted structural changes (shocks) in their environments. We consider the problem of the Airport Surface Planning for several simultaneously moving flights and two different approaches to it: the decentralised assemble of agents (one agent per flight) and Dispatcher agent (one agent controlling all steps of each flight). Both approaches were developed in Deep Q-Network (DQN) framework.

引用

页码：157 / 162

页数：6

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