Risk-sensitive and minimax control of discrete-time, finite-state Markov decision processes
This paper analyzes a connection between risk-sensitive and minimax criteria for discrete-time, finite state Markovian decision processes. The authors show dynamic programming for both criteria and synthesized optimal policies. Moreover, they formulate optimal risk-sensitive and minimax decision-making, in a general setting, which leads to stationary discounted optimal policies on the infinite horizon.
discrete-time, finite state Markovian decision processes
scientific article; zbMATH DE number 1325696