Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management

Abstract

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 preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain assets which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns.

References in corpus (10)

• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
• Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation
• Interpreting recurrent neural networks behaviour via excitable network attractors
• Reinforcement Learning with Convex Constraints
• Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data
• Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
• A Boolean Task Algebra for Reinforcement Learning
• Reinforcement Learning Your Way: Agent Characterization through Policy Regularization
• Can Interpretable Reinforcement Learning Manage Prosperity Your Way?
• Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality