Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Ceberio, Josu"'
Most Reinforcement Learning (RL) environments are created by adapting existing physics simulators or video games. However, they usually lack the flexibility required for analyzing specific characteristics of RL methods often relevant to research. Thi
Externí odkaz:
http://arxiv.org/abs/2407.03969
Autor:
Ceberio, Josu, Calvo, Borja
Experimentation is an intrinsic part of research in artificial intelligence since it allows for collecting quantitative observations, validating hypotheses, and providing evidence for their reformulation. For that reason, experimentation must be cohe
Externí odkaz:
http://arxiv.org/abs/2402.08298
Publikováno v:
Ann. Appl. Stat. 18(1): 42-62 (March 2024)
An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to be executed in the same mach
Externí odkaz:
http://arxiv.org/abs/2305.07345
Autor:
Santucci, Valentino, Ceberio, Josu
Problems with solutions represented by permutations are very prominent in combinatorial optimization. Thus, in recent decades, a number of evolutionary algorithms have been proposed to solve them, and among them, those based on probability models hav
Externí odkaz:
http://arxiv.org/abs/2304.02458
Recent advances in graph neural network architectures and increased computation power have revolutionized the field of combinatorial optimization (CO). Among the proposed models for CO problems, Neural Improvement (NI) models have been particularly s
Externí odkaz:
http://arxiv.org/abs/2206.00383
Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of problems using Neural Network models and Reinforcement Learning. Recently, its good performance has encouraged many practitioners to develop neural architectures
Externí odkaz:
http://arxiv.org/abs/2205.01356
Publikováno v:
Etor Arza, Josu Ceberio, Ekhi\~ne Irurozki & Aritz P\'erez (2022) Comparing Two Samples Through Stochastic Dominance: A Graphical Approach, Journal of Computational and Graphical Statistics
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outc
Externí odkaz:
http://arxiv.org/abs/2203.07889
Publikováno v:
In Expert Systems With Applications 1 September 2024 249 Part C
Autor:
González-Parra, Gilberto, Villanueva-Oller, Javier, Navarro-González, F.J., Ceberio, Josu, Luebben, Giulia
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena April 2024 181
This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL). To this end, we extend the Neural Combinatorial Optimization (NCO) theory in order to deal with constraints in its formu
Externí odkaz:
http://arxiv.org/abs/2006.11984