---
res:
  bibo_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.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Federico
      foaf_name: Cornalba, Federico
      foaf_surname: Cornalba
      foaf_workInfoHomepage: http://www.librecat.org/personId=2CEB641C-A400-11E9-A717-D712E6697425
    orcid: 0000-0002-6269-5149
  - foaf_Person:
      foaf_givenName: Constantin
      foaf_name: Disselkamp, Constantin
      foaf_surname: Disselkamp
  - foaf_Person:
      foaf_givenName: Davide
      foaf_name: Scassola, Davide
      foaf_surname: Scassola
  - foaf_Person:
      foaf_givenName: Christopher
      foaf_name: Helf, Christopher
      foaf_surname: Helf
  bibo_doi: 10.1007/s00521-023-09033-7
  bibo_issue: '2'
  bibo_volume: 36
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0941-0643
  - http://id.crossref.org/issn/1433-3058
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: 'Multi-objective reward generalization: Improving performance of Deep
    Reinforcement Learning for applications in single-asset trading@'
  fabio_hasPubmedId: '38187995'
...