no code implementations • 15 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.
1 code implementation • 13 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.
no code implementations • 4 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.
1 code implementation • 9 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.
no code implementations • 6 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.
no code implementations • 22 Jun 2020 • Ruben Solozabal, Josu Ceberio, Martin Takáč
This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL).