The potential and consistency of the Owen value for fuzzy cooperative games with a coalition structure

Li, Zijun; Meng, Fanyong

doi:10.1007/s10700-022-09397-w

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…• The survey of [50] only provides a detailed introduction to the original TransRL method, Decision Transformer [40], without any Transformer variants and practical application discussions. • The survey of [51] systematically summarizes various adversarial game models and the corresponding strategy generation approaches from a game theory perspective, and provides some real-world applications such as security, poker and politics. • The survey of [52] focuses on a particular application in the decision-making domain, the game AI system, considering three typical modern AI games (perfect in- formation games, imperfect information games, and multiagent games), providing a review of the relative solving techniques, paradigms and applications.…”

Section: B Related Surveysmentioning

confidence: 99%

Transformer in Reinforcement Learning for Decision-Making: A Survey

Yuan¹,

Chen²,

Chen³

et al. 2023

Preprint

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<p>Reinforcement learning (RL) has become a dominant decision-making paradigm and has achieved notable success in many real-world applications, such as autonomous driving, robotic manipulation, robotic navigation and gaming AI. However, the powerful abilities of the RL variants were developed with advanced machine learning techniques. For example, deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making tasks. Inspired by present day major successes of Transformer in natural language processing and computer vision, numerous bottlenecks have been overcome by combining Transformer with RL for decision-making. This paper presents a multiangle systematic survey of various Transformerbased RL (TransRL) models applied in decision-making tasks, including basic models, advanced algorithms, representative implementation instances, applications, and known challenges. Our work aims to provide insights into problems that inherently arise with the current RL approaches, and examines how we can address them with better TransRL models. To our knowledge, we are the first to present a comprehensive review of the recent Transformers research developments in RL for decisionmaking. We hope this survey provides a comprehensive review of TransRL models and also inspires the RL community in its pursuit of future directions. Finally, to keep track of the rapid TransRL developments in the decision-making domains, we summarize the latest relevant papers and their open-source implementations at https://github.com/williamyuanv0/Transformerin-Reinforcement-Learning-for-Decision-Making-A-Survey.</p>

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Section: B Related Surveysmentioning

confidence: 99%