Approximate receding horizon approach for Markov decision processes: average reward case
نویسندگان
چکیده
منابع مشابه
Approximate Receding Horizon Approach for Markov Decision Processes: Average Reward Case
We consider an approximation scheme for solving Markov Decision Processes (MDPs) with countable state space, finite action space, and bounded rewards that uses an approximate solution of a fixed finite-horizon sub-MDP of a given infinite-horizon MDP to create a stationary policy, which we call “approximate receding horizon control”. We first analyze the performance of the approximate receding h...
متن کاملAverage-Reward Decentralized Markov Decision Processes
Formal analysis of decentralized decision making has become a thriving research area in recent years, producing a number of multi-agent extensions of Markov decision processes. While much of the work has focused on optimizing discounted cumulative reward, optimizing average reward is sometimes a more suitable criterion. We formalize a class of such problems and analyze its characteristics, show...
متن کاملMarkov 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 کاملPseudometrics for State Aggregation in Average Reward Markov Decision Processes
We consider how state similarity in average reward Markov decision processes (MDPs) may be described by pseudometrics. Introducing the notion of adequate pseudometrics which are well adapted to the structure of the MDP, we show how these may be used for state aggregation. Upper bounds on the loss that may be caused by working on the aggregated instead of the original MDP are given and compared ...
متن کاملAverage Optimality in Nonhomogeneous Infinite Horizon Markov Decision Processes
We consider a nonhomogeneous stochastic infinite horizon optimization problem whose objective is to minimize the overall average cost per-period of an infinite sequence of actions (average optimality). Optimal solutions to such problems will in general be non-stationary. Moreover, a solution which initially makes poor decisions, and then selects wisely thereafter, can be average optimal. Howeve...
متن کاملذخیره 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: Journal of Mathematical Analysis and Applications
سال: 2003
ISSN: 0022-247X
DOI: 10.1016/s0022-247x(03)00506-7