Splitting Randomized Stationary Policies in Total-Reward Markov Decision Processes
نویسندگان
چکیده
منابع مشابه
Splitting Randomized Stationary Policies in Total-Reward Markov Decision Processes
This paper studies a discrete-time total-reward Markov decision process (MDP) with a given initial state distribution. A (randomized) stationary policy can be split on a given set of states if the occupancy measure of this policy can be expressed as a convex combination of the occupancy measures of stationary policies, each selecting deterministic actions on the given set and coinciding with th...
متن کاملQuantized Stationary Control Policies in Markov Decision Processes
For a large class of Markov Decision Processes, stationary (possibly randomized) policies are globally optimal. However, in Borel state and action spaces, the computation and implementation of even such stationary policies are known to be prohibitive. In addition, networked control applications require remote controllers to transmit action commands to an actuator with low information rate. Thes...
متن کاملEfficient Policies for Stationary Possibilistic Markov Decision Processes
Possibilistic Markov Decision Processes offer a compact and tractable way to represent and solve problems of sequential decision under qualitative uncertainty. Even though appealing for its ability to handle qualitative problems, this model suffers from the drowning effect that is inherent to possibilistic decision theory. The present paper proposes to escape the drowning effect by extending to...
متن کاملMarkov Decision Processes with Arbitrary Reward Processes
We consider a learning problem where the decision maker interacts with a standard Markov decision process, with the exception that the reward functions vary arbitrarily over time. We show that, against every possible realization of the reward process, the agent can perform as well—in hindsight—as every stationary policy. This generalizes the classical no-regret result for repeated games. Specif...
متن کاملNon-randomized policies for constrained Markov decision processes
This paper addresses constrained Markov decision processes, with expected discounted total cost criteria, which are controlled by nonrandomized policies. A dynamic programming approach is used to construct optimal policies. The convergence of the series of finite horizon value functions to the infinite horizon value function is also shown. A simple example illustrating an application is presented.
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: Mathematics of Operations Research
سال: 2012
ISSN: 0364-765X,1526-5471
DOI: 10.1287/moor.1110.0525