Search Results for author: Ruben Solozabal

Found 6 papers, 2 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

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

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.

Constrained Combinatorial Optimization with Reinforcement Learning

no code implementations22 Jun 2020 Ruben Solozabal, Josu Ceberio, Martin Takáč

This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL).

Combinatorial Optimization reinforcement-learning +1

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