Reinforcement Learning in Continuous Time and Space

摘要

This article presents a reinforcement learning framework for continuous- time dynamical systems without a priori discretization of time, state, and action. Based on the Hamilton-Jacobi-Bellman (HJB) equation for infinite- horizon, discounted reward problems, we derive algorithms for estimat- ing value functions and improving policies with the use of function ap- proximators.