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

arXiv: https://arxiv.org/abs/2203.04579 · HTML: https://mlsanity.com/paper/2203.04579/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Revisiting Fundamentals of Experience Replay](https://mlsanity.com/paper/2007.06700/) (2020)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Dynamic Weights in Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1809.07803/) (2018)
- [Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1610.02707/) (2016)
- [Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey](https://mlsanity.com/paper/1909.02964/) (2019)
- [A Multi-Objective Deep Reinforcement Learning Framework](https://mlsanity.com/paper/1803.02965/) (2018)
- [Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement Learning](https://mlsanity.com/paper/1809.06364/) (2018)
- [Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space](https://mlsanity.com/paper/1910.04729/) (2019)
- [Modular Multi-Objective Deep Reinforcement Learning with Decision Values](https://mlsanity.com/paper/1704.06676/) (2017)

## Built on by (most cited first, 2 of 2)

- [Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards](https://mlsanity.com/paper/2502.02619/) (2025)
- [An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading](https://mlsanity.com/paper/2309.00626/) (2023)

## 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.