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We investigate the potential of Multi-Objective, Deep Reinforcement Learningfor stock and cryptocurrency single-asset trading: in particular, we consider aMulti-Objective algorithm which generalizes the reward functions and discountfactor (i.e., these components are not specified a priori, but incorporated inthe learning process). Firstly, using several important assets (cryptocurrencypairs BTCUSD, ETHUSDT, XRPUSDT, and stock indexes AAPL, SPY, NIFTY50), weverify the reward generalization property of the proposed Multi-Objectivealgorithm, and provide preliminary statistical evidence showing increasedpredictive stability over the corresponding Single-Objective strategy.Secondly, we show that the Multi-Objective algorithm has a clear edge over thecorresponding Single-Objective strategy when the reward mechanism is sparse(i.e., when non-null feedback is infrequent over time). Finally, we discuss thegeneralization properties with respect to the discount factor. The entirety ofour code is provided in open source format.

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