# Dirichlet policies for reinforced factor portfolios

arXiv: https://arxiv.org/abs/2011.05381 · HTML: https://mlsanity.com/paper/2011.05381/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

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

- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
- [Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning](https://mlsanity.com/paper/2105.09264/) (2021)
- [Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning](https://mlsanity.com/paper/2404.10683/) (2024)

## Abstract (arXiv)

This article aims to combine factor investing and reinforcement learning (RL). The agent learns through sequential random allocations which rely on firms' characteristics. Using Dirichlet distributions as the driving policy, we derive closed forms for the policy gradients and analytical properties of the performance measure. This enables the implementation of REINFORCE methods, which we perform on a large dataset of US equities. Across a large range of parametric choices, our result indicates that RL-based portfolios are very close to the equally-weighted (1/N) allocation. This implies that the agent learns to be *agnostic* with regard to factors, which can partly be explained by cross-sectional regressions showing a strong time variation in the relationship between returns and firm characteristics.