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This document presents a study on using Uncertainty-Aware Reinforcement Learning (RL) for optimizing investment portfolios, addressing the challenges posed by market uncertainties and risk assessment. The authors propose a risk-constrained RL algorithm that utilizes a Variational Autoencoder (VAE) to estimate uncertainties and a Cost Network to backpropagate risk, demonstrating effectiveness in maintaining risk-averse strategies during portfolio optimization. The findings suggest that adopting this approach

Author

1150190050daisy

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EN