Results 61 to 70 of about 171,350 (313)
, 2020 Reinforcement Learning allows us to acquire knowledge without any training data. However, for learning it takes time. In this work, we propose a method to perform Reverse action by using Retrospective Kalman Filter that estimates the state one step before. We show an experience by a Hunter Prey problem. And discuss the usefulness of our proposed method.
Takao Miura, Kei Takahata
openaire +1 more source
Successive Over-Relaxation ${Q}$ -Learning [PDF]
IEEE Control Systems Letters, 2020 In a discounted reward Markov Decision Process (MDP), the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem reduces to solving a functional equation known as the Bellman equation and a fixed point iteration scheme known as the value iteration is utilized to obtain the solution. In
Chandramouli Kamanchi +2 more
openaire +2 more sources
Self-correcting Q-Learning [PDF]
arXiv, 2020 The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm to mitigate this bias.
arxiv
Molecular Oncology, EarlyView.There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes +20 more
wiley +1 more source
Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions
JOIV: International Journal on Informatics Visualization, 2020 Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by ...
Wasswa Shafik +3 more
doaj +1 more source
Cyberattack Correlation and Mitigation for Distribution Systems via Machine Learning
IEEE Open Access Journal of Power and Energy, 2023 Cyber-physical system security for electric distribution systems is critical. In direct switching attacks, often coordinated, attackers seek to toggle remote-controlled switches in the distribution network.
Jennifer Appiah-Kubi, Chen-Ching Liu
doaj +1 more source
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 more
wiley +1 more source
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan +6 more
wiley +1 more source
Risk analysis of the Unity 1.5T MR‐Linac adapt‐to‐shape workflow
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Background and Purpose The adapt‐to‐shape (ATS) workflow on the Unity MR‐Linac (Elekta AB, Stockholm, Sweden) allows for full replanning including recontouring and reoptimization5. Additional complexity to this workflow is added when the adaptation involves the use of MIM Maestro (MIM Software, Cleveland, OH) software in conjunction with ...
Jiayi Liang +13 more
wiley +1 more source