A random measure approach to reinforcement learning in continuous time

TL;DR

This paper introduces a novel random measure framework for modeling exploration in continuous-time reinforcement learning, enabling better theoretical analysis and algorithm development for controlled diffusion and jump processes.

Contribution

It develops a limit theorem for grid-sampled random measures, connecting discrete sampling to continuous-time models in RL with jumps and diffusion.

Findings

Proves convergence of grid-sampled measures to a limit SDE.

Shows the limit SDE can replace existing exploratory models.

Provides a foundation for analyzing and designing RL algorithms 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 substitute the exploratory SDE and the sample SDE of the recent continuous-time RL literature, i.e., it can be applied for the theoretical analysis of exploratory control problems and for the derivation of learning algorithms.