OPTIMAL CONTROL OF AVERAGE REWARD MARKOV DECISION PROCESSES' CONSTRAINED CONTINUOUS-TIME FINITE

The paper studies optimization of average-reward continuous-time finite state and action Markov Deci-sion Processes with multiple criteria and constraints. Under the standard unichain assumption, we prove the existence of optimal K-switching strategies for feasi-ble problems with K constraints. For switching ran-domized strategies, the decisions depend on the current state and the the time spent in the current state after the last jump. For stationary strategies, these functions do not depend on sojourn times, i.e. they are constant in time. For K-snitching strategies, these functions are piece-wise constant and the total number of jumps is limited by K. If there is no absorbing states, there exist also optimal K-randomized policies. We consider the linear programming approach and provide algorithms for calculations of optimal policies.