2,466 research outputs found
Markov Decision Processes and Stochastic Games with Total Effective Payoff
We consider finite Markov decision processes (MDPs) with undiscounted total effective payoff. We show that there exist uniformly optimal pure stationary strategies that can be computed by solving a polynomial
number of linear programs. We apply this result to two-player zero-sum stochastic games with perfect information and undiscounted total effective payoff, and derive the existence of a saddle point in uniformly optimal pure stationary strategies
A Nested Family of -total Effective Rewards for Positional Games
We consider Gillette's two-person zero-sum stochastic games with perfect
information. For each k \in \ZZ_+ we introduce an effective reward function,
called -total. For and this function is known as {\it mean
payoff} and {\it total reward}, respectively. We restrict our attention to the
deterministic case. For all , we prove the existence of a saddle point which
can be realized by uniformly optimal pure stationary strategies. We also
demonstrate that -total reward games can be embedded into -total
reward games
Mean-Field-Type Games in Engineering
A mean-field-type game is a game in which the instantaneous payoffs and/or
the state dynamics functions involve not only the state and the action profile
but also the joint distributions of state-action pairs. This article presents
some engineering applications of mean-field-type games including road traffic
networks, multi-level building evacuation, millimeter wave wireless
communications, distributed power networks, virus spread over networks, virtual
machine resource management in cloud networks, synchronization of oscillators,
energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201
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