# TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution

arXiv: https://arxiv.org/abs/2104.00620 · HTML: https://mlsanity.com/paper/2104.00620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [FeUdal Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1703.01161/) (2017)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?](https://mlsanity.com/paper/1909.10618/) (2019)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://mlsanity.com/paper/2004.06627/) (2020)
- [A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding](https://mlsanity.com/paper/2004.05940/) (2020)
- [Application of Deep Q-Network in Portfolio Management](https://mlsanity.com/paper/2003.06365/) (2020)
- [Deep Deterministic Portfolio Optimization](https://mlsanity.com/paper/2003.06497/) (2020)
- [Energy-based Surprise Minimization for Multi-Agent Value Factorization](https://mlsanity.com/paper/2009.09842/) (2020)

## Abstract (arXiv)

Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems.