Related papers: Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization

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An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm [0.0]
This scientific paper propose a novel portfolio optimization model using an improved deep reinforcement learning algorithm. The proposed algorithm is based on actor-critic architecture, in which the main task of critical network is to learn the distribution of portfolio cumulative return. A multi-process method is used, called Ape-x, to accelerate the speed of deep reinforcement learning training.
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