Results 21 to 30 of about 311 (117)
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,
Karan K. Budhraja, Tim Oates 0001
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Inverse Constrained Reinforcement Learning
CoRR, 2020 Camera-ready version for ICML ...
Usman Anwar +3 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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Inverse Reinforcement Learning for Marketing [PDF]
SSRN Electronic Journal, 2017 Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control.
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Inverse Reinforcement Learning with Constraint Recovery
, 2023 In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learner or agent seeks to recover the reward function of the MDP, given a set of trajectory demonstrations for the optimal policy.
Nirjhar Das, Arpan Chattopadhyay
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Cooperative Inverse Reinforcement Learning
CoRR, 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 ...
Dylan Hadfield-Menell +3 more
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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.
Qifeng Qiao, Peter A. Beling
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An Overview of Inverse Reinforcement Learning Techniques [PDF]
, 2021 In decision-making problems reward function plays an important role in finding the best policy. Reinforcement Learning (RL) provides a solution for decision-making problems under uncertainty in an Intelligent Environment (IE). However, it is difficult to specify the reward function for RL agents in large and complex problems.
Syed Ihtesham Hussain Shah +1 more
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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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