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Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties [PDF]
Frontiers in Robotics and AI, 2021 Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
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Password Guessing Model Based on Reinforcement Learning [PDF]
Jisuanji kexue, 2023 Password guessing is an important research direction in password security.Password guessing based on generative adversarial network(GAN) is a new method proposed in recent years,which guides the update of the generator according to evaluation results on ...
LI Xiaoling, WU Haotian, ZHOU Tao, LU Hui
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IEEE Access, 2023 This paper proposes a model estimation method in offline Bayesian model-based reinforcement learning (MBRL). Learning a Bayes-adaptive Markov decision process (BAMDP) model using standard variational inference often suffers from poor predictive ...
Toru Hishinuma, Kei Senda
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Applied Sciences, 2022 In recent years, the recommendation system and robot learning are undoubtedly the two most popular application fields, and the core algorithms supporting these two fields are deep learning based on perception and reinforcement learning based on ...
Huaidong Yu, Jian Yin
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Data-efficient model-based reinforcement learning with trajectory discrimination
Complex & Intelligent Systems, 2023 Deep reinforcement learning has always been used to solve high-dimensional complex sequential decision problems. However, one of the biggest challenges for reinforcement learning is sample efficiency, especially for high-dimensional complex problems ...
Tuo Qu +4 more
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Review of Model-Based Reinforcement Learning
Jisuanji kexue yu tansuo, 2020 Deep reinforcement learning (DRL) as an important learning paradigm in the field of machine learning, has received increasing attentions after AlphaGo defeats the human.
ZHAO Tingting, KONG Le, HAN Yajie, REN Dehua, CHEN Yarui
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Survey of Reinforcement Learning Based Recommender Systems [PDF]
Jisuanji kexue, 2021 Recommender systems are devoted to find and automatically recommend valuable information and services for users from massive data,which can effectively solve the information overload problem,and become an important information technology in the era of ...
YU Li, DU Qi-han, YUE Bo-yan, XIANG Jun-yao, XU Guan-yu, LENG You-fang
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eLife, 2021 Dopamine is implicated in representing model-free (MF) reward prediction errors a as well as influencing model-based (MB) credit assignment and choice. Putative cooperative interactions between MB and MF systems include a guidance of MF credit assignment
Lorenz Deserno +5 more
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A Unifying Framework for Reinforcement Learning and Planning
Frontiers in Artificial Intelligence, 2022 Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have ...
Thomas M. Moerland +4 more
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Entrainable Neural Conversation Model Based on Reinforcement Learning
IEEE Access, 2020 The synchronization of words in conversation, called entrainment, is generally observed in human-human conversations. Entrainment has a high correlation with dialogue success, naturalness, and engagement.
Seiya Kawano +3 more
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