Results 31 to 40 of about 88,537 (171)
Offline Inverse Reinforcement Learning
, 2021 The objective of offline RL is to learn optimal policies when a fixed exploratory demonstrations data-set is available and sampling additional observations is impossible (typically if this operation is either costly or rises ethical questions). In order to solve this problem, off the shelf approaches require a properly defined cost function (or its ...
Jarboui, Firas, Perchet, Vianney
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Inverse reinforcement learning with Gaussian process [PDF]
Proceedings of the 2011 American Control Conference, 2011 We present new algorithms for inverse reinforcement learning (IRL, or inverse optimal control) in convex optimization settings. We argue that finite-space IRL can be posed as a convex quadratic program under a Bayesian inference framework with the objective of maximum a posterior estimation.
Qiao, Qifeng, Beling, Peter A.
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Hybrid fuzzy AHP–TOPSIS approach to prioritizing solutions for inverse reinforcement learning
Complex & Intelligent Systems, 2022 Reinforcement learning (RL) techniques nurture building up solutions for sequential decision-making problems under uncertainty and ambiguity. RL has agents with a reward function that interacts with a dynamic environment to find out an optimal policy ...
Vinay Kukreja
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Triangle Inequality for Inverse Optimal Control
IEEE Access, 2023 Inverse optimal control (IOC) is a problem of estimating a cost function based on the behaviors of an expert that behaves optimally with respect to the cost function.
Sho Mitsuhashi, Shin Ishii
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Modeling sensory-motor decisions in natural behavior. [PDF]
PLoS Computational Biology, 2018 Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We
Ruohan Zhang +6 more
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Generative Adversarial Inverse Reinforcement Learning With Deep Deterministic Policy Gradient
IEEE Access, 2023 Although the issue of sparse expert samples at the early stage of training in inverse reinforcement learning (IRL) is successfully resolved by the introduction of generative adversarial network (GAN), the inherent drawbacks of GAN result in ineffective ...
Ming Zhan, Jingjing Fan, Jianying Guo
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Inverse Constrained Reinforcement Learning
, 2020 Camera-ready version for ICML ...
Anwar, Usman +3 more
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Regularized Inverse Reinforcement Learning
, 2020 26 pages, 7 ...
Jeon, Wonseok +5 more
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Applied Sciences, 2021 Despite its significant effectiveness in adversarial training approaches to multidomain task-oriented dialogue systems, adversarial inverse reinforcement learning of the dialogue policy frequently fails to balance the performance of the reward estimator ...
Jeiyoon Park +4 more
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Preference Elicitation and Inverse Reinforcement Learning [PDF]
, 2011 We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent's preferences, policy and optionally, the obtained reward ...
Rothkopf C.A., Dimitrakakis C.
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