mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Online Optimal Execution Strategies

arXiv 2410.13493 ↗First posted 17 Oct 2024cs.LG stat.MLcited by 2

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) to address this issue, with a focus on transient price impact modeled by a general decay kernel. Through numerical experiments with various decay kernels, we show that our algorithm successfully approximates the optimal execution strategy. Additionally, the proposed algorithm demonstrates adaptability to evolving market conditions, where parameters fluctuate over time. Our findings also show that modern reinforcement learning algorithms can provide a solution that reduces the need for frequent and inefficient human intervention in optimal execution tasks.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 22 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

3 of 22 references.

References · 3

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2018Double Deep Q-Learning for Optimal Executioncited by 43

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Executioncited by 0
  2. 2026Can Reinforcement Learning Efficiently Discover Price Manipulation?cited by 0

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  8. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey