Examinando por Materia "Aprendizaje por refuerzo"

Mostrando 1 - 3 de 3
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    Ítem
    A Machine Learning driven Energy Management System for tropical household prosumer nodes
    (Universidad EAFIT, 2024) Giraldo Pérez, Juan Pablo; Mejía Gutiérrez, Ricardo
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    Ítem
    Desarrollo de un algoritmo de aprendizaje por refuerzo profundo para resolver el despacho hidrotérmico colombiano considerando escenarios hidrológicos y de demanda bajo incertidumbre
    (Universidad EAFIT, 2022) Ramírez Arango, Alejandro; Aguilar Castro, José Lisandro
    Economic dispatch is a widely analyzed optimization problem in the electricity sector, which seeks to make the best use of available resources to meet demand at minimum cost. This problem has a great complexity in its solution due to the uncertainty of multiple parameters, being of special interest the hydrological uncertainty for the Colombian case due to its high dependence on hydroelectric plants. In this paper, we view economic dispatch as a multistage decision making problem and propose a Reinforcement Learning to solve the Colombian economic dispatch problem considering hydrological scenarios, due to its ability to handle uncertainty and sequential decisions. The policy performance of our algorithm is compared with classic deterministic method. The main advantage of our method is it can learn from a robust policy to deal the inflow and load demand scenarios.
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    Modelo de aprendizaje profundo reforzado aplicado al trading de Bitcoin
    (Universidad EAFIT, 2022) Obando Morales, Sebastián; Jaramillo Posada, Juan Rodrigo
    The stock market is affected by many types of factors, such as market sentiment, going upwards (bulls) or downwards (bears), the behavior of the economy, or unexpected political events. By For this reason, it is not possible to predict its behavior, which means that it is not possible to decide when to enter or when to exit with certainty. An approach such as deep reinforcement learning, which can emulate the experience of a negotiator (trader) who does not necessarily predict prices, but, market entry and exit times, would be a viable option. The present work sought to implement a reinforced deep learning approach to stock trading (bitcoins, stocks, and commodities), which has shown positive results in the literature with returns positive on investment. The bot, the result of this work, obtained a return of 5%. These positive results open the door to trying new approaches that include new combinations in the way of interpreting indicators to find winning strategies that increase profitability.