Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for selected applications in stock and cryptocurrency trading
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We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency trading. More specifically, we build on the generalized setting à la Fontaine and Friedman arXiv:1809.06364 (where the reward weighting mechanism is not specified a priori, but embedded in the learning process) by complementing it with computational speed-ups, and adding the cumulative reward's discount factor to the learning process. Firstly, we verify that the resulting Multi-Objective algorithm generalizes well, and we provide preliminary statistical evidence showing that its prediction is more stable than the corresponding Single-Objective strategy's. 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 of the discount factor. The entirety of our code is provided in open source format.
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