mlsanityData to 14 Oct 2026

An Actor-Critic Framework for Continuous-Time Jump-Diffusion Controls with Normalizing Flows

arXiv 2604.05398 ↗First posted 7 Apr 2026math.OC cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

Continuous-time stochastic control with time-inhomogeneous jump-diffusion dynamics is central in finance and economics, but computing optimal policies is difficult under explicit time dependence, discontinuous shocks, and high dimensionality. We propose an actor-critic framework that serves as a mesh-free solver for entropy-regularized control problems and stochastic games with jumps. The approach is built on a time-inhomogeneous little q-function and an appropriate occupation measure, yielding a policy-gradient representation that accommodates time-dependent drift, volatility, and jump terms. To represent expressive stochastic policies in continuous-action spaces, we parameterize the actor using conditional normalizing flows, enabling flexible non-Gaussian policies while retaining exact likelihood evaluation for entropy regularization and policy optimization. We validate the method on time-inhomogeneous linear-quadratic control, Merton portfolio optimization, and a multi-agent portfolio game, using explicit solutions or high-accuracy benchmarks. Numerical results demonstrate stable learning under jump discontinuities, accurate approximation of optimal stochastic policies, and favorable scaling with respect to dimension and number of agents.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 24 of its 41 references that are in the map. First posted on arXiv 7 Apr 2026.

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24 of 41 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Accuracy of Discretely Sampled Stochastic Policies in Continuous-time Reinforcement Learning13 Mar
  2. 2024Robust Reinforcement Learning under Diffusion Models for Data with Jumps18 Nov
  3. 2024Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy4 Jul
  4. 2024Learning Optimal Deterministic Policies with Stochastic Policy Gradients3 May
  5. 2024Control randomisation approach for policy gradient and application to reinforcement learning in optimal switching27 Apr

Other references · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  4. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  5. 2019Neural Spline Flowscited by 596
  6. 2021Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithmscited by 101
  7. 2022q-Learning in Continuous Timecited by 91
  8. 2021Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approachcited by 84
  9. 2019Making Deep Q-learning methods robust to time discretizationcited by 58
  10. 2023Policy Optimization for Continuous Reinforcement Learningcited by 31
  11. 2021Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networkscited by 29
  12. 2021Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controlscited by 24
7 more
  1. 2023Continuous-time q-learning for mean-field control problemscited by 22
  2. 2024FlowPG: Action-constrained Policy Gradient with Normalizing Flowscited by 16
  3. 2024Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machinecited by 9
  4. 2025Deep Learning for Continuous-Time Stochastic Control with Jumpscited by 6
  5. 2023Entropy-Regularized Mean-Variance Portfolio Optimization with Jumpscited by 5
  6. 2024Martingale deep neural network for very high-dimensional stochastic optimal controlscited by 2
  7. 2024Multi-Agent Relative Investment Games in a Jump Diffusion Market with Deep Reinforcement Learning Algorithmcited by 1

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  1. 2024Reinforcement Learning for Jump-Diffusions, with Financial Applications
  2. 2024Solving Time-Continuous Stochastic Optimal Control Problems: Algorithm Design and Convergence Analysis of Actor-Critic Flow
  3. 2026Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems
  4. 2026Entropy Regularized Reinforcement Learning for Zero-Sum Stochastic Differential Games in a Regime-Switching Jump-Diffusion Process
  5. 2026Reinforcement Learning for Continuous-Time Jump Markov Decision Processes with Applications to Network Dynamic Pricing
  6. 2026Reinforcement Learning for Risk-Sensitive Investment Management: a Free Energy--Entropy Duality Approach
  7. 2025Convergence of an actor-critic gradient flow for entropy regularised MDPs in general spaces
  8. 2025Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes