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of 18
pro vyhledávání: '"Haghani, Naveed"'
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
Multi-Agent Reinforcement Learning (MARL) struggles with sample inefficiency and poor generalization [1]. These challenges are partially due to a lack of structure or inductive bias in the neural networks typically used in learning the policy. One su
Externí odkaz:
http://arxiv.org/abs/2410.02581
This paper presents primarily two Euclidean embeddings of the quotient space generated by matrices that are identified modulo arbitrary row permutations. The original application is in deep learning on graphs where the learning task is invariant to n
Externí odkaz:
http://arxiv.org/abs/2203.07546
In this article we introduce Graph Generation, an enhanced Column Generation (CG) algorithm for solving expanded linear programming relaxations of mixed integer linear programs. To apply Graph Generation, we must be able to map any given column to a
Externí odkaz:
http://arxiv.org/abs/2110.01070
We consider the problem of accelerating column generation (CG) for logistics optimization problems using vehicle routing as an example. Without loss of generality, we focus on the Capacitated Vehicle Routing Problem (CVRP) via the addition of a new c
Externí odkaz:
http://arxiv.org/abs/2108.09233
Autor:
Haghani, Naveed, Li, Jiaoyang, Koenig, Sven, Kunapuli, Gautam, Contardo, Claudio, Regan, Amelia, Yarkony, Julian
Robots performing tasks in warehouses provide the first example of wide-spread adoption of autonomous vehicles in transportation and logistics. The efficiency of these operations, which can vary widely in practice, are a key factor in the success of
Externí odkaz:
http://arxiv.org/abs/2103.08835
Autor:
Haghani, Naveed, Li, Jiaoyang, Koenig, Sven, Kunapuli, Gautam, Contardo, Claudio, Yarkony, Julian
We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific constraints. We formulate the problem as a weighted set packing problem whe
Externí odkaz:
http://arxiv.org/abs/2006.04856
We address the problem of accelerating column generation for set cover problems in which we relax the state space of the columns to do efficient pricing. We achieve this by adapting the recently introduced smooth and flexible dual optimal inequalitie
Externí odkaz:
http://arxiv.org/abs/2004.05499
We address the problem of accelerating column generation (CG) for set-covering formulations via dual optimal inequalities (DOI). DOI use knowledge of the dual solution space to derive inequalities that might be violated by intermediate solutions to a
Externí odkaz:
http://arxiv.org/abs/2001.02267
Autor:
Haghani, Naveed
This work addresses the challenge of stabilizing column generation (CG) via dual optimal inequalities (DOI). We present two novel classes of DOI for the general context of set cover problems. We refer to these as Smooth DOI (S-DOI) and Flexible DOI (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7624bbe64970ed497f5c387e8c58bffe
Autor:
Balan, Radu, Haghani, Naveed
Publikováno v:
Proceedings of SPIE; 9/9/2019, Vol. 11138, p1-11, 11p