@article{14451,
  abstract     = {We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency single-asset trading: in particular, we consider a Multi-Objective algorithm which generalizes the reward functions and discount factor (i.e., these components are not specified a priori, but incorporated in the learning process). Firstly, using several important assets (BTCUSD, ETHUSDT, XRPUSDT, AAPL, SPY, NIFTY50), we verify the reward generalization property of the proposed Multi-Objective algorithm, and provide preliminary statistical evidence showing increased predictive stability over the corresponding Single-Objective strategy. Secondly, we show that the Multi-Objective algorithm has a clear edge over the corresponding Single-Objective strategy when the reward mechanism is sparse (i.e., when non-null feedback is infrequent over time). Finally, we discuss the generalization properties with respect to the discount factor. The entirety of our code is provided in open-source format.},
  author       = {Cornalba, Federico and Disselkamp, Constantin and Scassola, Davide and Helf, Christopher},
  issn         = {1433-3058},
  journal      = {Neural Computing and Applications},
  publisher    = {Springer Nature},
  title        = {{Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading}},
  doi          = {10.1007/s00521-023-09033-7},
  year         = {2023},
}