Portfolio optimization involves determining the optimal allocation of
portfolio assets in order to maximize a given investment objective.
Traditionally, some form of mean-variance optimization is used with the aim of
maximizing returns while minimizing risk, however, more recently, deep
reinforcement learning formulations have been explored. Increasingly, investors
have demonstrated an interest in incorporating ESG objectives when making
investment decisions, and modifications to the classical mean-variance
optimization framework have been developed. In this work, we study the use of
deep reinforcement learning for responsible portfolio optimization, by
incorporating ESG states and objectives, and provide comparisons against
modified mean-variance approaches. Our results show that deep reinforcement
learning policies can provide competitive performance against mean-variance
approaches for responsible portfolio allocation across additive and
multiplicative utility functions of financial and ESG responsibility
objectives.

本研究旨在研究深度强化学习在负责任投资组合优化中的应用，通过纳入环境、社会和治理评估指标，并与修改后的均值 - 方差优化方法进行比较，结果表明深度强化学习策略在满足投资收益和负责任投资目标的加性和乘性效用函数方面表现出有竞争力的表现。