IEEE Transactions on Automatic Control, Vol.42, No.8, 1163-1170, 1997
The Role of Information State and Adjoint in Relating Nonlinear Output-Feedback Risk-Sensitive Control and Dynamic-Games
This paper employs logarithmic transformations to establish relations between continuous-time nonlinear partially observable risk-sensitive control problems and analogous output feedback dynamic games, The first logarithmic transformation is introduced to relate the stochastic information state to a deterministic information state, The second logarithmic transformation is applied to the risk-sensitive cost function using the Laplace-Varadhan lemma, In the small noise limit, this cost function is shown to be logarithmically equivalent to the cost function of an analogous dynamic game.
Keywords:MAXIMUM PRINCIPLE;SYSTEMS