Transformers in Reinforcement Learning: A Survey
Transformers have significantly impacted domains like natural language processing, computer vision, and robotics, where they improve performance compared to other neural networks. This survey explores how transformers are used in reinforcement learning (RL), where they are seen as a promising solution for addressing challenges such as unstable training, credit assignment, lack of interpretability, and partial observability. We begin by providing a brief domain overview of RL, followed by a discussion on the challenges of classical RL algorithms. Next, we delve into the properties of the transformer and its variants and discuss the characteristics that make them well-suited to address the challenges inherent in RL. We examine the application of transformers to various aspects of RL, including representation learning, transition and reward function modeling, and policy optimization. We also discuss recent research that aims to enhance the interpretability and efficiency of transformers in RL, using visualization techniques and efficient training strategies. Often, the transformer architecture must be tailored to the specific needs of a given application. We present a broad overview of how transformers have been adapted for several applications, including robotics, medicine, language modeling, cloud computing, and combinatorial optimization. We conclude by discussing the limitations of using transformers in RL and assess their potential for catalyzing future breakthroughs in this field.
Code (0)
등록된 구현이 없습니다.
Tasks
Cloud ComputingCombinatorial OptimizationLanguage ModelingLanguage Modellingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Representation LearningSurveySimilar Papers 제목 키워드 기반
A Survey on Transformers in Reinforcement Learning
Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)SurveyOn the Universality of Transformer Architectures; How Much Attention Is Enough?
Transformers are crucial across many AI fields, such as large language models, computer vision, and reinforcement learning. This prominence stems from the architecture's perceived universality and scalability compared to…
Reinforcement LearningA survey of the Vision Transformers and their CNN-Transformer based Variants
Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationsh…
SurveyTransformers in Remote Sensing: A Survey
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures, originally introduced in natural languag…
SurveyEfficient Transformers: A Survey
Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning. In the field of natural language processing for…
Navigatereinforcement-learningReinforcement Learning (RL)Survey