mlsanityData to 7 Oct 2026

Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset trading

arXiv 2203.04579 ↗First posted 9 Mar 2022cs.LG q-fin.CP q-fin.TRcited by 2

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Abstract (arXiv)

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 (cryptocurrency pairs BTCUSD, ETHUSDT, XRPUSDT, and stock indexes 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 58 references that are in the map. First posted on arXiv 9 Mar 2022.

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13 of 58 references.

References · 13

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2017Hindsight Experience Replaycited by 1,096
  3. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 240
  4. 2014A Survey of Multi-Objective Sequential Decision-Makingcited by 229
  5. 2020Revisiting Fundamentals of Experience Replaycited by 171
  6. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 123
  7. 2018Dynamic Weights in Multi-Objective Deep Reinforcement Learningcited by 96
  8. 2016Multi-Objective Deep Reinforcement Learningcited by 81
  9. 2019Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Surveycited by 45
  10. 2018A Multi-Objective Deep Reinforcement Learning Frameworkcited by 22
  11. 2018Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement Learningcited by 5
  12. 2019Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Spacecited by 3
1 more
  1. 2017Modular Multi-Objective Deep Reinforcement Learning with Decision Valuescited by 1

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2025Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewardscited by 1
  2. 2023An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Tradingcited by 0