Faster Deep Q-learning using Neural Episodic Control

被引：4

作者：

Nishio, Daichi ^{[1
]}

Yamane, Satoshi ^{[1
]}

机构：

[1] Kanazawa Univ, Inst Sci & Engn, Kanazawa, Ishikawa, Japan

来源：

2018 IEEE 42ND ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 1 | 2018年

关键词：

Deep reinforcement learning; DQN; Neural Episodic Control; Sample efficiency;

D O I：

10.1109/COMPSAC.2018.00075

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The research on deep reinforcement learning which estimates Q-value by deep learning has been attracted the interest of researchers recently. In deep reinforcement learning, it is important to efficiently learn the experiences that an agent has collected by exploring environment. We propose NEC2DQN that improves learning speed of a poor sample efficiency algorithm such as DQN by using good one such as NEC at the beginning of learning. We show it is able to learn faster than Double DQN or N-step DQN in the experiments of Pong.

引用

页码：486 / 491

页数：6

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