FINITE HORIZON MARKOV GAMES WITH RECURSIVE PAYOFF SYSTEMS

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Markov Games: Receding Horizon Approach

We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with infinite horizon discounted cost and average cost criteria. We first present error bounds from the optimal equilibrium value of the game when both players take correlated equilibrium receding horizon policies that are based on exact or approximate solutions of receding finite horizon subg...

متن کامل

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 sadd...

متن کامل

Finite-horizon variance penalised Markov decision processes

We consider a finite horizon Markov decision process with only terminal rewards. We describe a finite algorithm for computing a Markov deterministic policy which maximises the variance penalised reward and we outline a vertex elimination algorithm which can reduce the computation involved.

متن کامل

Acquired Cooperation in Finite-Horizon Games

When a prisoner’s-dilemma-like game is repeated any finite number of times, the only equilibrium outcome is the one in which all players defect in all periods. However, if cooperation among the players changes their perception of the game by making defection increasingly less attractive, then players may be willing to cooperate in late periods in which unilateral defection has become unprofitab...

متن کامل

Finite-Horizon Markov Decision Processes with State Constraints

Markov Decision Processes (MDPs) have been used to formulate many decision-making problems in science and engineering. The objective is to synthesize the best decision (action selection) policies to maximize expected rewards (minimize costs) in a given stochastic dynamical environment. In many practical scenarios (multi-agent systems, telecommunication, queuing, etc.), the decision-making probl...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Memoirs of the Faculty of Science, Kyushu University. Series A, Mathematics

سال: 1975

ISSN: 0373-6385

DOI: 10.2206/kyushumfs.29.123