# Reinforced Deep Markov Models With Applications in Automatic Trading

arXiv: https://arxiv.org/abs/2011.04391 · HTML: https://mlsanity.com/paper/2011.04391/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [On the Stochastic Stability of Deep Markov Models](https://mlsanity.com/paper/2111.04601/) (2021)
- [Deep Belief Markov Models for POMDP Inference](https://mlsanity.com/paper/2503.13438/) (2025)
- [Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization](https://mlsanity.com/paper/2212.14670/) (2022)
- [Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance](https://mlsanity.com/paper/2201.02397/) (2022)

## Abstract (arXiv)

Inspired by the developments in deep generative models, we propose a model-based RL approach, coined Reinforced Deep Markov Model (RDMM), designed to integrate desirable properties of a reinforcement learning algorithm acting as an automatic trading system. The network architecture allows for the possibility that market dynamics are partially visible and are potentially modified by the agent's actions. The RDMM filters incomplete and noisy data, to create better-behaved input data for RL planning. The policy search optimisation also properly accounts for state uncertainty. Due to the complexity of the RKDF model architecture, we performed ablation studies to understand the contributions of individual components of the approach better. To test the financial performance of the RDMM we implement policies using variants of Q-Learning, DynaQ-ARIMA and DynaQ-LSTM algorithms. The experiments show that the RDMM is data-efficient and provides financial gains compared to the benchmarks in the optimal execution problem. The performance improvement becomes more pronounced when price dynamics are more complex, and this has been demonstrated using real data sets from the limit order book of Facebook, Intel, Vodafone and Microsoft.