Can Interpretable Reinforcement Learning Manage Prosperity Your Way?

2022-02-18 · Charl Maree, Christian Omlin

Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers' needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and understanding of customers. Post-hoc approaches are typically used for explaining pretrained reinforcement learning models. Based on our previous modeling of customer spending behaviour, we adapt our recent reinforcement learning algorithm that intrinsically characterizes desirable behaviours and we transition to the problem of asset management. We train inherently interpretable reinforcement learning agents to give investment advice that is aligned with prototype financial personality traits which are combined to make a final recommendation. We observe that the trained agents' advice adheres to their intended characteristics, they learn the value of compound growth, and, without any explicit reference, the notion of risk as well as improved policy convergence.

📄 PDF Abstract BibTeX arXiv:2202.09064

Code (0)

등록된 구현이 없습니다.

Tasks

Asset ManagementDecision Making Management reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management

2022-04-20 · Charl Maree, Christian W. Omlin

The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other…

Asset Management Management reinforcement-learning Reinforcement Learning+1

Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models

2022-08-26 · Charl Maree, Christian W. Omlin

The proliferation of artificial intelligence is increasingly dependent on model understanding. Understanding demands both an interpretation - a human reasoning about a model's behavior - and an explanation - a symbolic r…

Management reinforcement-learning Reinforcement Learning (RL)

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management

2023-03-18 · Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach

The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on developing dynamic inventory ordering pol…

Additive models Management reinforcement-learning Reinforcement Learning+1

Computerized enrollment system project report

2024-10-20 · ResearchGate 2024 10 · Kamal Acharya

The project entitled “COMPUTERIZED ENROLLMENT SYSTEM”. Every organization, whether big or small, has human resource challenges to overcome. Every organization has different employee management needs; therefore we design …

Management

Econometric Approach to Analyzing Determinants of Sustained Prosperity

2023-12-22 · Anika Dixit

Every year, substantial resources are allocated to foreign aid with the aim of catalyzing prosperity and development in recipient countries. The diverse body of research on the relationship between aid and gross domestic…

Econometrics