mlsanityData to 14 Oct 2026

Reinforcement Learning for Speculative Trading under Exploratory Framework

arXiv 2604.02035 ↗First posted 2 Apr 2026q-fin.MF cs.LG math.OCcited by 0

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

Abstract (arXiv)

We study a speculative trading problem within the exploratory reinforcement learning (RL) framework of Wang et al. [2020]. The problem is formulated as a sequential optimal stopping problem over entry and exit times under general utility function and price process. We first consider a relaxed version of the problem in which the stopping times are modeled by the jump times of Cox processes driven by bounded, non-randomized intensity controls. Under the exploratory formulation, the agent's randomized control is characterized via the probability measure over the jump intensities, and their objective function is regularized by Shannon's differential entropy. This yields a system of the exploratory HJB equations and Gibbs distributions in closed-form as the optimal policy. Error estimates and convergence of the RL objective to the value function of the original problem are established. Finally, an RL algorithm is designed, and its implementation is showcased in a pairs-trading application.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 34 references that are in the map. First posted on arXiv 2 Apr 2026.

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  1. 2024Learning to Optimally Stop Diffusion Processes, with Financial Applications17 Aug

Other references · 15

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
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  3. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  4. 2017DGM: A deep learning algorithm for solving partial differential equationscited by 678
  5. 2017Bridging the Gap Between Value and Policy Based Reinforcement Learningcited by 182
  6. 2017A unified view of entropy-regularized Markov decision processescited by 182
  7. 2015Taming the Noise in Reinforcement Learning via Soft Updatescited by 127
  8. 2021Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithmscited by 101
  9. 2022q-Learning in Continuous Timecited by 91
  10. 2020Entropy Regularization for Mean Field Games with Learningcited by 58
  11. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
  12. 2020Deep learning of free boundary and Stefan problemscited by 35
3 more
  1. 2021Exploratory HJB equations and their convergencecited by 32
  2. 2019Solving high-dimensional optimal stopping problems using deep learningcited by 22
  3. 2022Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithmcited by 17

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