Stock market prediction using DQN with DQNReg loss function
There have been many developments in predicting stock market prices usingreinforcement learning. Recently, Google released a paper that designed a new loss function,specifically for meta-learning reinforcement learning. In this paper, implementation is doneusing this loss function to the reinforcement learning model, whose objective is to predict thestock price based on certain parameters. The reinforcement learning used is an encoderdecoderframework that is useful for extracting features from long sequence prices. TheDQNReg loss function is implemented in the encoder-decoder model as it could providestrong adaptation performance in a variety of settings. The model can buy and sell the index, and the reward is the portfolio return after the days trading has concluded. To maximizeyield the model must optimize reward function. The DQNReg loss implemented DQN network and the Huber loss DQN network is compared with the Sharpe ratio considered for return. 2024 The Author(s).
Artificial Intelligence, Blockchain, Computing and Security - Proceedings of the International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023, Vol-1, pp. 58-63.
https://doi.org/10.1201/9781003393580-10 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186684014&doi=10.1201%2f9781003393580-10&partnerID=40&md5=affeb80f0aeacad64370be9350a3f613
Sebastian A., Department of Computer Science Christ University, Bangalore, India; Habis K.V., Department of Computer Science Christ University, Bangalore, India; Shukla S., Department of Computer Science Christ University, Bangalore, India