Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Thibaut Théate"'
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 2453-2462 (2023)
The energy transition is expected to significantly increase the share of renewable energy sources whose production is intermittent in the electricity mix. Apart from key benefits, this development has the major drawback of generating a mismatch betwe
Externí odkaz:
https://doaj.org/article/594bd3256e8e4aa0b37133d42ae4f701
Autor:
Thibaut Théate, Damien Ernst
Publikováno v:
Algorithms, Vol 16, Iss 7, p 325 (2023)
Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk a
Externí odkaz:
https://doaj.org/article/ff42163ea0b7452780a96a86130457be
Publikováno v:
Energies, Vol 13, Iss 23, p 6435 (2020)
Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricit
Externí odkaz:
https://doaj.org/article/7e30a428c0d044e9865a88e2e24883dc
Autor:
Ernst, Thibaut Théate, Damien
Publikováno v:
Algorithms; Volume 16; Issue 7; Pages: 325
Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::a6dd253c9adcb6f9ddf4c98f691b0ad4
Autor:
Damien Ernst, Thibaut Théate
This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock mar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d6ff511b45ec70526a1d75fb74dd272
