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Cao, Z. and Lin, C.-T. (2021) Reinforcement Learning from Hierarchical Critics. IEEE Transactions on Neural Networks and Learning Systems, 1-8.
https://doi.org/10.1109/TNNLS.2021.3103642

has been cited by the following article:

• TITLE: Reinforcement Learning Toolkits for Gaming: A Comparative Qualitative Analysis

  AUTHORS: Mehdi Mekni, Charitha Sree Jayaramireddy, Sree Veera Venkata Sai Saran Naraharisetti

  KEYWORDS: Game Design & Development, Machine Learning, Reinforcement Learning, Deep Learning

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.15 No.12, December 30, 2022

  ABSTRACT: Historically viewed as a niche economic sector, gaming is now projected to exceed a global annual revenue of $218.7 billion in 2024, taking advantage of recent Artificial Intelligence (AI) advances. In recent years, specific AI techniques namely; Machine Learning (ML) and Reinforcement Learning (RL), have seen impressive progress and popularity. Techniques developed within these two fields are now able to analyze and learn from gameplay experiences enabling more interactive, immersive, and engaging games. While the number of ML and RL algorithms is growing, their implementations through frameworks and toolkits are also extensive too. Moreover, the game design and development community lacks a framework for informed evaluation of available RL toolkits. In this paper, we present a comprehensive survey of RL toolkits for games using a qualitative evaluation methodology.