Results 1 to 10 of about 311 (117)
Mathematics, 2020 Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of ...
Francisco Martinez-Gil +5 more
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Agronomy, 2022 A series-parallel hybrid banana-harvesting robot was previously developed to pick bananas, with inverse kinematics intractable to an address. This paper investigates a deep reinforcement learning-based inverse kinematics solution to guide the banana ...
Guichao Lin +5 more
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Dynamic Service Composition Method Based on Zero-Sum Game Integrated Inverse Reinforcement Learning
IEEE Access, 2023 Automatically generating service composition solutions that meet user application requirements is one of the hot research topics in the field of service composition in the context of Web service big data. To address the challenges of accurately obtaining
Yuan Yuan, Yuhan Guo, Wanqing Ma
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Machine Teaching for Human Inverse Reinforcement Learning
Frontiers in Robotics and AI, 2021 As robots continue to acquire useful skills, their ability to teach their expertise will provide humans the two-fold benefit of learning from robots and collaborating fluently with them.
Michael S. Lee +2 more
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Meta-inverse Reinforcement Learning Method Based on Relative Entropy [PDF]
Jisuanji kexue, 2021 Aiming at the problem that traditional inverse reinforcement learning algorithms are slow,imprecise,or even unsolvable when solving the reward function owing to insufficient expert demonstration samples and unknown state transition probabilitie,a meta ...
WU Shao-bo, FU Qi-ming, CHEN Jian-ping, WU Hong-jie, LU You
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Gaussian processes non‐linear inverse reinforcement learning
IET Cyber-systems and Robotics, 2021 The authors analyse a Bayesian framework for posing and solving inverse reinforcement learning (IRL) problems that arise in decision‐making and optimisation settings.
Qifeng Qiao, Xiaomin Lin
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A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
Applied Sciences, 2023 The continuous path of a manipulator is often discretized into a series of independent action poses during path tracking, and the inverse kinematic solution of the manipulator’s poses is computationally challenging and yields inconsistent results.
Pengyu Zhang, Jie Zhang, Jiangming Kan
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Neural scalarisation for multi-objective inverse reinforcement learning
SICE Journal of Control, Measurement, and System Integration, 2023 Multi-objective inverse reinforcement learning (MOIRL) extends inverse reinforcement learning (IRL) to multi-objective problems by estimating weights and multi-objective rewards to help retrain and analyse preference-conditioned behaviour.
Daiko Kishikawa, Sachiyo Arai
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AIP Advances, 2022 Here, we report a case study on inverse design of quantum dot optical spectra using a deep reinforcement learning algorithm for the desired target optical property of semiconductor CdxSeyTex−y quantum dots. Machine learning models were trained to predict
Hibiki Yoshida +6 more
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Detecting Physiological Needs Using Deep Inverse Reinforcement Learning
Applied Artificial Intelligence, 2022 Smart health-care assistants are designed to improve the comfort of the patient where smart refers to the ability to imitate the human intelligence to facilitate his life without, or with limited, human intervention. As a part of this, we are proposing a
Khaoula Hantous +2 more
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