Percentile performance criteria for limiting average Markov decision processes
نویسندگان
چکیده
منابع مشابه
Markov Decision Processes with Average-Value-at-Risk criteria
P We investigate the problem of minimizing the Average-Value-at-Risk (AV aRτ ) of the discounted cost over a finite and an infinite horizon which is generated by a Markov Decision Process (MDP). We show that this problem can be reduced to an ordinary MDP with extended state space and give conditions under which an optimal policy exists. We also give a time-consistent interpretation of the AV aR...
متن کاملPercentile Optimization for Markov Decision Processes with Parameter Uncertainty
Markov decision processes are an effective tool in modeling decision-making in uncertain dynamic environments. Since the parameters of these models are typically estimated from data or learned from experience, it is not surprising that the actual performance of a chosen strategy often significantly differs from the designer’s initial expectations due to unavoidable modeling ambiguity. In this p...
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 کاملl AVERAGE COST SEMI - MARKOV DECISION PROCESSES
^ The Semi-Markov Decision model is considered under the criterion of long-run average cost. A new criterion, which for any policy considers the limit of the expected cost Incurred during the first n transitions divided by the expected length of the first n transitions, is considered. Conditions guaranteeing that an optimal stationary (nonrandomized) policy exist are then presented. It is also ...
متن کاملPercentile Queries in Multi-dimensional Markov Decision Processes
Markov decision processes (MDPs) with multi-dimensional weights are useful to analyze systems with multiple objectives that may be conflicting and require the analysis of trade-offs. We study the complexity of percentile queries in such MDPs and give algorithms to synthesize strategies that enforce such constraints. Given a multi-dimensional weighted MDP and a quantitative payoff function f , t...
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: IEEE Transactions on Automatic Control
سال: 1995
ISSN: 0018-9286,1558-2523
DOI: 10.1109/9.362904