论文标题
在异质概况中进行最佳投资和储蓄策略选择的深入强化学习：智能代理人努力退休
Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement
论文作者
论文摘要
从定义的福利到定义的缴款计划的过渡使从政府和机构退休的责任转移到了个人。确定个人的最佳储蓄和投资策略对于稳定的金融立场和避免在工作生活和退休期间避免贫困至关重要，这在一个世界上，这是一项特别具有挑战性的任务，在这个世界上，不同职业群体经历的就业和收入轨迹的形式是高度多样化的。我们介绍了一个模型，在该模型中，代理商学习适合其异质概况的最佳投资组合分配和储蓄策略。我们使用深度加强学习来训练代理。通过职业和年龄依赖收入演变动态校准环境。该研究的重点是取决于代理概况的异质收入轨迹，并结合了代理的行为参数化。该模型提供了一种灵活的方法，可在不同的情况下估算异构概况的终身消费和投资选择。
The transition from defined benefit to defined contribution pension plans shifts the responsibility for saving toward retirement from governments and institutions to the individuals. Determining optimal saving and investment strategy for individuals is paramount for stable financial stance and for avoiding poverty during work-life and retirement, and it is a particularly challenging task in a world where form of employment and income trajectory experienced by different occupation groups are highly diversified. We introduce a model in which agents learn optimal portfolio allocation and saving strategies that are suitable for their heterogeneous profiles. We use deep reinforcement learning to train agents. The environment is calibrated with occupation and age dependent income evolution dynamics. The research focuses on heterogeneous income trajectories dependent on agent profiles and incorporates the behavioural parameterisation of agents. The model provides a flexible methodology to estimate lifetime consumption and investment choices for heterogeneous profiles under varying scenarios.
