# Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations

arXiv: https://arxiv.org/abs/2201.01874 · HTML: https://mlsanity.com/paper/2201.01874/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 3 of 3)

- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations](https://mlsanity.com/paper/1904.06387/) (2019)
- [Taming the Noise in Reinforcement Learning via Soft Updates](https://mlsanity.com/paper/1512.08562/) (2015)

## Built on by (most cited first, 3 of 3)

- [Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards](https://mlsanity.com/paper/2502.02619/) (2025)
- [Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading](https://mlsanity.com/paper/2307.09377/) (2023)
- [Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/2608.15770/) (2026)

## Abstract (arXiv)

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.