Results 51 to 60 of about 88,537 (171)
Adaptive Actuation of Magnetic Soft Robots Using Deep Reinforcement Learning
Advanced Intelligent Systems, 2023 Magnetic soft robots (MSRs) have attracted growing interest due to their unique advantages in untethered actuation and excellent controllability. However, actuation strategies of these robots have long been designed out of heuristics. Herein, it is aimed
Jianpeng Yao +6 more
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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 +5 more
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Cooperative Inverse Reinforcement Learning
, 2016 For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning ...
Hadfield-Menell, Dylan +3 more
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Misspecification in Inverse Reinforcement Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from a policy pi. To do this, we need a model of how pi relates to R. In the current literature, the most common models are optimality, Boltzmann rationality, and causal entropy maximisation. One of the primary motivations behind IRL is to infer human preferences from human
Skalse, Joar, Abate, Alessandro
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Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
, 2018 In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the ...
Brown, Daniel S., Niekum, Scott
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Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods
, 2018 The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral.
Lacotte, Jonathan +3 more
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Reinforcement Learning with a Corrupted Reward Channel
, 2017 No real-world reward function is perfect. Sensory errors and software bugs may result in RL agents observing higher (or lower) rewards than they should.
Everitt, Tom +4 more
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Inverse Reinforcement Learning in Large State Spaces via Function Approximation [PDF]
, 2017 This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation
Burdick, Joel W., Li, Kun
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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
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Информатика и автоматизацияUsing 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 +2 more
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