Performance evaluation of q-learning algorithms

Myeonghwi Kim, Beakcheol Jang

Research output: Contribution to journalArticlepeer-review

Abstract

Q-learning is the most representative algorithm of reinforcement learning, and it is a method of making the best choice by utilizing the compensation obtained through interaction between environment and agent. However, existing Q-learning has a problem of performance degradation when the environment is complicated, there are multi-agents, or the memory load is low. To solve these problems, various algorithms such as deep Q-learning, modular Q-learning, and Nash Q-learning have been developed. In this paper, we evaluate their performances through grid world experiments. Experiments compare Q-learning, deep Q-learning, modular Q-learning and Nash Q-learning. Q-learning performs better than deep Q-learning in simple problems. However, for more difficult problems, Q-learning is limited, and deep Q-learning is more efficient. Nash Q-learning and modular Q-learning show similar performance. However, Nash Q-learning has better performance for more difficult problems.

Original languageEnglish
Pages (from-to)181-192
Number of pages12
JournalJP Journal of Heat and Mass Transfer
Volume2019
Issue numberSpecial Issue 1
DOIs
Publication statusPublished - 2019 Oct

Bibliographical note

Publisher Copyright:
© 2019 Pushpa Publishing House, Prayagraj, India.

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

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