Results 21 to 30 of about 88,537 (171)
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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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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Neuroevolution-based Inverse Reinforcement Learning [PDF]
2017 IEEE Congress on Evolutionary Computation (CEC), 2017 The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution,
Budhraja, Karan K., Oates, Tim
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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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Quantum generative adversarial imitation learning
New Journal of Physics, 2023 Investigating quantum advantage in the NISQ era is a challenging problem whereas quantum machine learning becomes the most promising application that can be resorted to.
Tailong Xiao +4 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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Objective Weight Interval Estimation Using Adversarial Inverse Reinforcement Learning
IEEE Access, 2023 Several real-world problems are modeled as multi-objective sequential decision-making problems with multiple competing objectives, and multi-objective reinforcement learning (MORL) has garnered attention as a solution to this problem.
Naoya Takayama, Sachiyo Arai
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Lifelong Inverse Reinforcement Learning
, 2022 Published in NeurIPS 2018.
Mendez, Jorge A. +2 more
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Inverse reinforcement learning from summary data [PDF]
Machine Learning, 2018 To appear in ECMLPKDD ...
Kaski Samuel, Kangasrääsiö Antti
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