Results 11 to 20 of about 88,537 (171)
Off-Dynamics Inverse Reinforcement Learning
IEEE AccessImitation learning is a widely-used paradigm for decision making that learns from expert demonstrations. Existing imitation algorithms often require multiple interactions between the agent and the environment from which the demonstration is obtained. The
Yachen Kang, Jinxin Liu, Donglin Wang
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Material hardship, not household income, predicts impaired punishment learning: a computational reinforcement learning perspective [PDF]
Frontiers in PsychologyIntroductionSocioeconomic disadvantage has been linked to neurocognitive alterations in reward and loss processing, which may contribute to adverse psychological outcomes.
Zhen Wang +5 more
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Compatible Reward Inverse Reinforcement Learning [PDF]
, 2017 Inverse Reinforcement Learning (IRL) is an effective approach to recover a reward function that explains the behavior of an expert by observing a set of demonstrations. This paper is about a novel model-free IRL approach that, differently from most of the existing IRL algorithms, does not require to specify a function space where to search for the ...
Metelli, ALBERTO MARIA +2 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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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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Inverse reinforcement learning in contextual MDPs [PDF]
Machine Learning, 2021 AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent.
Stav Belogolovsky +4 more
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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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Inverse Reinforcement Learning without Reinforcement Learning
, 2023 Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) problem as a subroutine. This is counter-
Swamy, Gokul +3 more
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Option compatible reward inverse reinforcement learning [PDF]
Pattern Recognition Letters, 2022 This paper is under consideration at Pattern Recognition ...
Hwang, Rakhoon +2 more
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