Abstract
SUMMARY This paper is devoted to the study of the connections among risk-sensitive stochastic optimal control, dynamic game optimal control, risk-neutral stochastic optimal control and deterministic optimal control in a nonlinear, discrete-time context with complete state information. The analysis worked out sheds light on the profound links among these control strategies, which remain hidden in the linear context. In particular, it is shown that, under suitable parameterizations, risk-sensitive control can be regarded as a control methodology which combines features of both stochastic risk-neutral control and deterministic dynamic game control.
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