Multi-Objective Inverse Reinforcement Learning via Non-Negative Matrix Factorization
Author:
Affiliation:
1. Chiba University,Department of Urban Environment Systems,Chiba,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9790893/9790720/09790941.pdf?arnumber=9790941
Reference22 articles.
1. Inverse Optimization
2. Inverse multi-objective combinatorial optimization
3. Learning to Drive in a Day
4. Optimality and non-scalar-valued performance criteria
5. Algorithms for inverse reinforcement learning;ng;ICML,2000
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1. The rise of nonnegative matrix factorization: Algorithms and applications;Information Systems;2024-07
2. Neural scalarisation for multi-objective inverse reinforcement learning;SICE Journal of Control, Measurement, and System Integration;2023-04
3. Objective Weight Interval Estimation Using Adversarial Inverse Reinforcement Learning;IEEE Access;2023
4. Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation;2022 61st Annual Conference of the Society of Instrument and Control Engineers (SICE);2022-09-06
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