  This paper presents a new theory, known as robust dynamic pro- gramming, for
a class of continuous-time dynamical systems. Different from traditional
dynamic programming (DP) methods, this new theory serves as a fundamental tool
to analyze the robustness of DP algorithms, and in par- ticular, to develop
novel adaptive optimal control and reinforcement learning methods. In order to
demonstrate the potential of this new framework, four illustrative applications
in the fields of stochastic optimal control and adaptive DP are presented.
Three numerical examples arising from both finance and engineering industries
are also given, along with several possible extensions of the proposed
framework.
