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pro vyhledávání: '"Hu, Yuanquan"'
Autor:
Zhang, Hengxi, Shi, Zhendong, Hu, Yuanquan, Ding, Wenbo, Kuruoglu, Ercan E., Zhang, Xiao-Ping
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this context, reinforcement learning (RL), which operates on a reward-centri
Externí odkaz:
http://arxiv.org/abs/2303.11959
The marriage between mean-field theory and reinforcement learning has shown a great capacity to solve large-scale control problems with homogeneous agents. To break the homogeneity restriction of mean-field theory, a recent interest is to introduce g
Externí odkaz:
http://arxiv.org/abs/2209.04808
Publikováno v:
In Journal of the Franklin Institute December 2023 360(18):14783-14805
Publikováno v:
In Fuel 15 January 2023 332 Part 2
Autor:
Zhang, Hengxi, Shi, Zhendong, Hu, Yuanquan, Ding, Wenbo, Kuruoglu, Ercan E., Zhang, Xiao-Ping
Due to the rapid dynamics and a mass of uncertainties in the quantitative markets, the issue of how to take appropriate actions to make profits in stock trading remains a challenging one. Reinforcement learning (RL), as a reward-oriented approach for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ef1bd4c9560d365b8e9d206960d53ec
http://arxiv.org/abs/2303.11959
http://arxiv.org/abs/2303.11959