  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.
