A Random Measure Approach To Reinforcement Learning In Continuous Time

Abstract

We present a random measure approach for modeling exploration, i.e., the execution of measure-valued controls, in continuous-time reinforcement learning (RL) with controlled diffusion and jumps. First, we consider the case when sampling the randomized control in continuous time takes place on a discrete-time grid and reformulate the resulting stochastic differential equation (SDE) as an equation driven by suitable random measures. The construction of these random measures makes use of the Brownian motion and the Poisson random measure (which are the sources of noise in the original model dynamics) as well as the additional random variables, which are sampled on the grid for the control execution. Then, we prove a limit theorem for these random measures as the mesh-size of the sampling grid goes to zero, which leads to the grid-sampling limit SDE that is jointly driven by white noise random measures and a Poisson random measure. We also argue that the grid-sampling limit SDE can substit

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keybender2024a

Related papers

• Exploration Versus Exploitation In Reinforcement Learning: A Stochastic Control Approach (2018)9.76
• Accuracy Of Discretely Sampled Stochastic Policies In Continuous-time Reinforcement Learning (2025)0.00
• Robust Reinforcement Learning Under Diffusion Models For Data With Jumps (2024)0.00
• Efficient Exploration In Continuous-time Model-based Reinforcement Learning (2023)0.00
• Deterministic Policy Gradient For Reinforcement Learning With Continuous Time And State (2025)0.00
• Sublinear Regret For A Class Of Continuous-time Linear-quadratic Reinforcement Learning Problems (2024)0.00
• Taming "data-hungry" Reinforcement Learning? Stability In Continuous State-action Spaces (2024)2.26
• Distributional Hamilton-jacobi-bellman Equations For Continuous-time Reinforcement Learning (2022)0.00