# An Offline Learning Approach to Propagator Models

arXiv: https://arxiv.org/abs/2309.02994 · HTML: https://mlsanity.com/paper/2309.02994/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [MOReL : Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2005.05951/) (2020)
- [Is Pessimism Provably Efficient for Offline RL?](https://mlsanity.com/paper/2012.15085/) (2020)
- [Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage](https://mlsanity.com/paper/2107.06226/) (2021)
- [Dealing with the Unknown: Pessimistic Offline Reinforcement Learning](https://mlsanity.com/paper/2111.05440/) (2021)
- [Statistical Learning with Sublinear Regret of Propagator Models](https://mlsanity.com/paper/2301.05157/) (2023)
- [Equilibrium in Functional Stochastic Games with Mean-Field Interaction](https://mlsanity.com/paper/2306.05433/) (2023)

## Built on by (most cited first, 2 of 2)

- [Exploratory Optimal Stopping: A Singular Control Formulation](https://mlsanity.com/paper/2408.09335/) (2024)
- [Solving Optimal Execution Problems via In-Context Operator Networks](https://mlsanity.com/paper/2501.15106/) (2025)

## Abstract (arXiv)

We consider an offline learning problem for an agent who first estimates an unknown price impact kernel from a static dataset, and then designs strategies to liquidate a risky asset while creating transient price impact. We propose a novel approach for a nonparametric estimation of the propagator from a dataset containing correlated price trajectories, trading signals and metaorders. We quantify the accuracy of the estimated propagator using a metric which depends explicitly on the dataset. We show that a trader who tries to minimise her execution costs by using a greedy strategy purely based on the estimated propagator will encounter suboptimality due to so-called spurious correlation between the trading strategy and the estimator and due to intrinsic uncertainty resulting from a biased cost functional. By adopting an offline reinforcement learning approach, we introduce a pessimistic loss functional taking the uncertainty of the estimated propagator into account, with an optimiser which eliminates the spurious correlation, and derive an asymptotically optimal bound on the execution costs even without precise information on the true propagator. Numerical experiments are included to demonstrate the effectiveness of the proposed propagator estimator and the pessimistic trading strategy.