Monopoly Using Reinforcement Learning

被引：0

作者：

Arun, Edupuganti ^{[1
]}

Rajesh, Harikrishna ^{[1
]}

Chakrabarti, Debarka ^{[1
]}

Cherala, Harikiran ^{[1
]}

George, Koshy ^{[1
]}

机构：

[1] PES Univ, Dept ECE, Bangalore, Karnataka, India

来源：

PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY | 2019年

关键词：

Q-Learning; feedforward neural network; reinforcement learning; RECOGNITION;

D O I：

10.1109/tencon.2019.8929523

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper elucidates a machine learning model that learns to play a popular board game, namely monopoly. This model is trained and tested against various players, each with different strategies. The model applies a feedforward neural network with the concept of experience replay to learn to play the game. The model and this paper helps to reinforce the idea that there is no one strategy that will always win against any other strategy, while maintaining high win-rates.

引用

页码：864 / 868

页数：5

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