Search Results for author: Martin Takac

Found 14 papers, 5 papers with code

Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows

no code implementations15 Feb 2024 Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac

This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery.


Reinforcement Learning for Solving Stochastic Vehicle Routing Problem

1 code implementation13 Nov 2023 Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac

This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions.

reinforcement-learning Reinforcement Learning (RL)

Regularization of the policy updates for stabilizing Mean Field Games

no code implementations4 Apr 2023 Talal Algumaei, Ruben Solozabal, REDA ALAMI, Hakim Hacid, Merouane Debbah, Martin Takac

This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns.

Multi-agent Reinforcement Learning reinforcement-learning

MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids

1 code implementation15 Mar 2023 Nicolas Cuadrado, Roberto Gutierrez, Yongli Zhu, Martin Takac

Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability.

Multi-agent Reinforcement Learning Total Energy

Learning to generalize Dispatching rules on the Job Shop Scheduling

1 code implementation9 Jun 2022 Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takac

Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem.

Job Shop Scheduling Scheduling

Learning to Control under Time-Varying Environment

no code implementations6 Jun 2022 Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takac, Bin Gu

To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.

Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input

no code implementations10 Mar 2022 Guangyi Liu, Arash Amini, Martin Takac, Nader Motee

For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.


Distributed Learning With Sparsified Gradient Differences

no code implementations5 Feb 2022 Yicheng Chen, Rick S. Blum, Martin Takac, Brian M. Sadler

A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.

Improving Text-to-Image Synthesis Using Contrastive Learning

1 code implementation6 Jul 2021 Hui Ye, Xiulong Yang, Martin Takac, Rajshekhar Sunderraman, Shihao Ji

To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images.

Contrastive Learning Text-to-Image Generation

On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches

no code implementations14 Jul 2018 Jie Liu, Yu Rong, Martin Takac, Junzhou Huang

This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.

Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption

no code implementations16 Dec 2016 Jie Liu, Martin Takac

We propose a projected semi-stochastic gradient descent method with mini-batch for improving both the theoretical complexity and practical performance of the general stochastic gradient descent method (SGD).

BIG-bench Machine Learning

Matrix Completion under Interval Uncertainty

no code implementations11 Aug 2014 Jakub Marecek, Peter Richtarik, Martin Takac

Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints.

Collaborative Filtering Matrix Completion

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