2020
DOI: 10.3233/ds-200028
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Reinforcement learning for personalization: A systematic literature review

Abstract: The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we intro… Show more

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Cited by 28 publications
(16 citation statements)
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References 211 publications
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“…Many practical limitations arise in traditional domains such as healthcare, making these benefits listed above fade away [4,5,9,10]. Such limitations are the inaccessibility to large samples of data, the unavailability of environments to train and evaluate algorithms in, the limitations on the data caused by privacy laws, and safety concerns (e.g., unsafe actions and exploration), explainability, and legal responsibility [4,10]. As a consequence, the applicability of DRL in many practical tasks remains limited.…”
Section: Arxiv:210315908v2 [Csai] 31 Mar 2021mentioning
confidence: 99%
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“…Many practical limitations arise in traditional domains such as healthcare, making these benefits listed above fade away [4,5,9,10]. Such limitations are the inaccessibility to large samples of data, the unavailability of environments to train and evaluate algorithms in, the limitations on the data caused by privacy laws, and safety concerns (e.g., unsafe actions and exploration), explainability, and legal responsibility [4,10]. As a consequence, the applicability of DRL in many practical tasks remains limited.…”
Section: Arxiv:210315908v2 [Csai] 31 Mar 2021mentioning
confidence: 99%
“…Reinforcement Learning for Clinical Applications A literature review has shown that the number of applications of RL has been increasing [10]. Applications in healthcare range from treating patients with Sepsis at the Intensive Care Unit to sending personalized messages in e-Health mobile applications.…”
Section: Related Workmentioning
confidence: 99%
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“…Healthcare. Recently DRL has gained traction for applications such as personalized healthcare treatments [236]. Liu et al [237] proposed the first DRL framework for estimating the optimal dynamic treatment regimes from observational medical data using DRL to estimate the long term value function.…”
Section: A Deep Reinforcement Learning Applicationsmentioning
confidence: 99%
“… Written as a teaching book and is more than a review article  Useful for those who wish to learn about deep learning, reinforcement learning, how they combine as deep-RL and their applications in the real world [45]  Reviewed the basic algorithms of deep reinforcement learning in terms of research method, deep learning model involving RL and their application in various fields (e.g., AlphaGo, robotics and natural language processing)  General review of all reinforcement applications [46]  Addressed the use of deep learning in region-based and region-free detection frameworks for object grasping [47]  Presented the application of deep learning methods to generalised robotic grasp detection [48]  Presented robotics-specific learning, reasoning and embodiment challenges in deep learning [49]  Focused on the extreme end of the spectrum: how robots can acquire the learning capability through only a handful of trials and a few minutes; referred to this challenge as "micro-data reinforcement learning" [32]  Reviewed deep reinforcement learning-based intelligent soft robotics, including various algorithms, and examples of their application in real-world scenarios [50]  Highlighted the recent reinforcement learning algorithms used in robot manipulation  Small range of topics due to the lack of papers pertaining to manipulation systems [51]  Presented an intensive study of vision-based robotic grasp detection methods [52]  Presented a survey of studies that used machine learning for manipulation  Formalised the robot manipulation learning problem [53]  Introduced a framework of personalisation settings and used it in a systematic literature review  Reviewed existing solutions and evaluated the corresponding strategies [54]  Identified and discussed different aspects that influence a robotics task requiring the nontrivial use of the concept of affordances Previous reviews have summarized different topics that are associated with robotics, as indicated in Table 1. The two most closely related topics to robotic manipulation were discussed in [50] and [52].…”
Section: Figure 2 Learning Algorithms-based Reinforcement Learningmentioning
confidence: 99%