Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations

TL;DR

This paper introduces a method combining Inverse Reinforcement Learning and Reinforcement Learning to learn fund managers' investment strategies and improve asset allocation recommendations, outperforming individual managers.

Contribution

The paper presents a novel approach that integrates IRL and RL to capture fund managers' intent and enhance asset allocation decisions.

Findings

The method successfully learns fund managers' implied reward functions.

It improves asset allocation performance over individual fund managers.

The approach effectively combines human expertise with AI optimization.

Abstract

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.