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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, Scott A. Nivison, Zachary I. Bell, Rushikesh Kamalapurkar   +3 more
doaj   +2 more sources

A survey on model-based reinforcement learning

Science China Information Sciences
Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in playing complex video games that allow huge trial-and-error, making errors is always undesired in the real world.
Weinan Zhang, Xiong-Hui Chen, Tian Xu
exaly   +4 more sources

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
doaj   +1 more source

Model-based Reinforcement Learning: A Survey

Foundations and Trends® in Machine Learning, 2023
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This survey is an integration of both fields, better known as model-based reinforcement learning.
Moerland, T.M., Broekens, D.J., Plaat, A., Jonker, C.M.   +3 more
openaire   +3 more sources

Importance-Weighted Variational Inference Model Estimation for Offline Bayesian Model-Based Reinforcement Learning

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
doaj   +1 more source

Deep Reinforcement Factorization Machines: A Deep Reinforcement Learning Model with Random Exploration Strategy and High Deployment Efficiency

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
doaj   +1 more source

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, Fuqing Duan, Junge Zhang, Bo Zhao, Wenzhen Huang   +4 more
doaj   +1 more source

Model-Based Reinforcement Learning with SINDy

CoRR, 2022
8 pages, 1 figure, 1 table, 1 algorithm, presented at the Decision Awareness in Reinforcement Learning workshop held at the International Conference on Machine Learning, 22 July 2022, Baltimore MD ...
Rushiv Arora, Bruno Castro da Silva, Eliot Moss   +2 more
openaire   +3 more sources

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
doaj   +1 more source

The ubiquity of model-based reinforcement learning [PDF]

Current Opinion in Neurobiology, 2012
The reward prediction error (RPE) theory of dopamine (DA) function has enjoyed great success in the neuroscience of learning and decision-making. This theory is derived from model-free reinforcement learning (RL), in which choices are made simply on the basis of previously realized rewards. Recently, attention has turned to correlates of more flexible,
Bradley B, Doll, Dylan A, Simon, Nathaniel D, Daw   +2 more
openaire   +2 more sources