no code implementations • 16 Feb 2021 • Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis, Yuri Lawryshyn
We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem.
Hierarchical Reinforcement Learning reinforcement-learning +1
1 code implementation • 16 Sep 2020 • Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis, Yuri Lawryshyn
(2) EMIX highlights a practical use of energy functions in MARL with theoretical guarantees and experiment validations of the energy operator.
no code implementations • 8 Sep 2020 • Karthik Raja Kalaiselvi Bhaskar, Deepa Kundur, Yuri Lawryshyn
The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations.