Results 51 to 60 of about 311 (117)

Energy management of hybrid electric vehicles based on inverse reinforcement learning

Energy Reports, 2022
Many scholars have conducted research on reinforcement learning in energy management, and verified that reinforcement learning methods have certain advantages.
Hengxu Lv, Chunyang Qi, Chuanxue Song, Shixin Song, Ruiqiang Zhang, Feng Xiao   +5 more
doaj   +1 more source

Reinforcement Learning-Based Inverse Design of Multilayer Particles

Computation
Multilayered particles possess exceptional optical properties and hold significant potential for applications in chemical analysis, life sciences, optical sensing, and photonic integration.
Zhaohui Li, Fang Gao, Delian Liu
doaj   +1 more source

Neural computations underlying inverse reinforcement learning in the human brain

eLife, 2017
In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent.
Sven Collette, Wolfgang M Pauli, Peter Bossaerts, John O'Doherty   +3 more
doaj   +1 more source

Implicit Understanding: Decoding Swarm Behaviors in Robots through Deep Inverse Reinforcement Learning

Информатика и автоматизация
Using reinforcement learning to generate the collective behavior of swarm robots is a common approach. Yet, formulating an appropriate reward function that aligns with specific objectives remains a significant challenge, particularly as the complexity of
Alaa Iskandar, Ali Hammoud, Béla Kovács   +2 more
doaj   +1 more source

Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning

Journal of Advanced Transportation, 2020
The present study proposes a framework for learning the car-following behavior of drivers based on maximum entropy deep inverse reinforcement learning. The proposed framework enables learning the reward function, which is represented by a fully connected
Yang Zhou, Rui Fu, Chang Wang
doaj   +1 more source

Deep reinforcement learning for inverse inorganic materials design

npj Computational Materials
A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis spaces.
Christopher Karpovich, Elton Pan, Elsa A. Olivetti   +2 more
doaj   +1 more source

Contingency-based flexibility mechanisms through a reinforcement learning model in adults with attention-deficit/hyperactivity disorder and obsessive-compulsive disorder

Comprehensive Psychiatry
Background and aims: Impaired cognitive flexibility is associated with the characteristic symptomatology of ADHD and OCD. However, the mechanisms underlying learning and flexibility under uncertainty in adults with OCD or ADHD remain unclear.
Rocío Rodríguez-Herrera, José Juan León, Pilar Fernández-Martín, Ana Sánchez-Kuhn, Miguel Soto-Ontoso, Laura Amaya-Pascasio, Patricia Martínez-Sánchez, Pilar Flores   +7 more
doaj   +1 more source

Optimized heteronuclear-ion quantum demodulator with inverse-quartic and inverse-quadratic temporal scalings

Communications Physics
Efficient and precise measurement of electromagnetic signals is crucial for both fundamental science and practical applications. Demodulation is a widely used technique for receiving and recovering modulated signals.
Jiawei Zhang, Wenqiang Ding, Haojie Du, Jintao Bu, Wenfei Yuan, Bin Wang, Wenjin Chen, Liang Chen, Jiachong Li, Geyi Ding, Fei Zhou, Qing-Shou Tan, Mang Feng   +12 more
doaj   +1 more source

Thinking as human: Self-reflective reinforcement learning framework for fertilization decision-making

Smart Agricultural Technology
Variable-rate fertilization based on reinforcement learning has achieved significant success in simulated environments. However, issues of insecurity and inefficiency led by blind exploration mechanism constitute impediments to apply reinforcement ...
Shulang Li, Jie Liu, Yang Yang, Rujia Shen, Jingchi Jiang   +4 more
doaj   +1 more source

Optimizing Reinforcement Learning with Limited HRI Demonstrations: A Task-Oriented Weight Update Method with Analysis of Multi-head and Layer Feature Combinations

International Journal of Computational Intelligence Systems
To address the challenge of training reinforcement learning (RL) networks with limited data in Human-Robot Interaction (HRI), we introduce a novel task-oriented update method that combines meta-inverse reinforcement learning (Meta-IRL) and transformer ...
Qinghua Chen, Jessica Korneder, Osamah A. Rawashdeh, Yanfeng Wang, Wing-Yue Geoffrey Louie   +4 more
doaj   +1 more source