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Stochastic competitive learning in complex networks
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCPISCATAWAY, 2012)
Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized ...
Network-based stochastic semisupervised learning
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCPISCATAWAY, 2012)
Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a ...
Collusion and artificial intelligence : a computational experiment with sequential pricing algorithms under stochastic costs
(Universidad de San Andrés. Departamento de Economía, 2021-11)
Firms increasingly delegate their strategic decisions to algorithms. A potential concern
is that algorithms may undermine competition by leading to pricing outcomes that are
collusive, even without having been designed ...
Multiobjective design of sustainable public transportation systems
(Matrosov Institute for System Dynamics and Control Theory, 2020)
The design of the bus network is a complex problem in modern cities, since different conflicting objectives have to be considered, from both the perspective of bus companies and the citizens. This article presents a ...
Design and implementation of a system to determine tax evasion through de stochastic techniques
(Corporación Universidad de la Costa, 2021)
Meta-level reasoning in reinforcement learning
(Pontifícia Universidade Católica do Rio Grande do SulPorto Alegre, 2014)
Reinforcement learning (RL) é uma técnica para encontrar uma política ótima em ambientes estocásticos onde, as ações de uma política inicial são simuladas (ou executadas diretamente) e o valor de um estado é atualizado com ...
Meta-level reasoning in reinforcement learning
(Pontifícia Universidade Católica do Rio Grande do SulPorto Alegre, 2014)
Reinforcement learning (RL) é uma técnica para encontrar uma política ótima em ambientes estocásticos onde, as ações de uma política inicial são simuladas (ou executadas diretamente) e o valor de um estado é atualizado com ...
Comparative study of SAC and PPO in multi-agent reinforcement learning using unity ML-agents
(Universidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y Computación, 2023-07-25)
This document presents a comparative study of the Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms in the context of Multi-Agent Reinforcement Learning (MARL) using the Unity ML-Agents framework. ...
Particle Swarm Model Selection
(Journal of Machine Learning Research, 2009)
Particle Swarm Model Selection
(Journal of Machine Learning Research, 2009)