Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908518940934144 |
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| author | Chen, Yang Lin, Xiao Yan, Bo Zhang, Libo Liu, Jiamou Tan, Neset Özkan Witbrock, Michael |
| author_facet | Chen, Yang Lin, Xiao Yan, Bo Zhang, Libo Liu, Jiamou Tan, Neset Özkan Witbrock, Michael |
| contents | Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations. While promising, the assumption of agent homogeneity limits the capability of existing methods to handle demonstrations with heterogeneous and unknown objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method. Critically, our method can infer rewards from different yet structurally similar tasks without prior knowledge about underlying contexts or modifying the MFG model itself. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables Chen, Yang Lin, Xiao Yan, Bo Zhang, Libo Liu, Jiamou Tan, Neset Özkan Witbrock, Michael Machine Learning Artificial Intelligence Computer Science and Game Theory Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations. While promising, the assumption of agent homogeneity limits the capability of existing methods to handle demonstrations with heterogeneous and unknown objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method. Critically, our method can infer rewards from different yet structurally similar tasks without prior knowledge about underlying contexts or modifying the MFG model itself. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs. |
| title | Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables |
| topic | Machine Learning Artificial Intelligence Computer Science and Game Theory |
| url | https://arxiv.org/abs/2509.03845 |