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https://arxiv.org/html/2608.03138v1
Struct-Aware Policy Learning
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https://arxiv.org/html/2608.03545v1
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arXiv:2608
https://arxiv.org/html/2407.20004v1
our cities
Road Networks.
Traffic simulator.
Navigation services APIs.
Modified fastest route.
GPS Data.
Data and Code availability.
Acknowledgements.
Author contributions
Corresponding authors
A Supplementary Figures
B Supplementary
https://arxiv.org/html/2608.03141v1
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arXiv:2608
https://arxiv.org/html/2608.03655v1
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arXiv:2608
https://arxiv.org/list/gr-qc/new
We observe that each emitted graviton carries away an angular momentum of $\dot{J}/\dot {N}\simeq 2\hbar$, not only for elliptical orbits (as has already been pointed out by Page [arXiv:2409
https://arxiv.org/html/2407.08254v2
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Multi-agent planning with attrition
4 Attritable MCTS with Regret Minimization
4.1 Overview of the A-MCTS algorithm
4.2 Regret Matching For Cooperative Coordination
4.3 Analysis and
https://arxiv.org/html/2407.20441v1
I Introduction
II System Model and Problem Formulation
III Main result
IV Proof of the main result
V Simulations
VI Conclusion and Future Work
Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
Nicolò Dal Fabbro, Arman Adibi
https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator