Add to ORKGValue iteration is a commonly used and an empirically competitive method in solving many Markov decision process problems. However, it is known that value iteration has only pseudopolynomial complexity in general. We establish a somewhat surprising polynomial bound for value iteration on deterministic Markov decision (DMDP) problems. We show that the basic value iteration procedure converges to the highest average reward cycle on a DMDP problem in (n iterations, or (mn ) total time, where n denotes the number of states, and m the number of edges. We giv
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solu...
This research focuses on Markov Decision Processes (MDP). MDP is one of the most important and chall...
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solu...
Value iteration is a commonly used and em-pirically competitive method in solving many Markov decisi...
Value iteration is a fundamental algorithm for solving Markov Decision Processes (MDPs). It computes...
Abstract. Markov Decision Processes (MDP) are a widely used model including both non-deterministic a...
Markov Decision Processes (MDP) are a widely used model including both non-deterministic and probabi...
Value iteration is a fundamental algorithm for solving Markov Decision Processes (MDPs). It computes...
We prove that the simplex method with the highest gain/most-negative-reduced cost pivoting rule conv...
The question of knowing whether the Policy Iteration algorithm (PI) for solving Markov Decision Proc...
Solving Markov Decision Processes is a recurrent task in engineering which can be performed efficien...
This article proposes a three-timescale simulation based algorithm for solution of infinite horizon ...
International audienceMarkov Decision Processes (MDP) are a widely used model including both non-det...
Partially observable Markov decision processes (POMDPs) have recently become pop-ular among many AI ...
We present a technique for speeding up the convergence of value iteration for partially observable M...
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solu...
This research focuses on Markov Decision Processes (MDP). MDP is one of the most important and chall...
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solu...
Value iteration is a commonly used and em-pirically competitive method in solving many Markov decisi...
Value iteration is a fundamental algorithm for solving Markov Decision Processes (MDPs). It computes...
Abstract. Markov Decision Processes (MDP) are a widely used model including both non-deterministic a...
Markov Decision Processes (MDP) are a widely used model including both non-deterministic and probabi...
Value iteration is a fundamental algorithm for solving Markov Decision Processes (MDPs). It computes...
We prove that the simplex method with the highest gain/most-negative-reduced cost pivoting rule conv...
The question of knowing whether the Policy Iteration algorithm (PI) for solving Markov Decision Proc...
Solving Markov Decision Processes is a recurrent task in engineering which can be performed efficien...
This article proposes a three-timescale simulation based algorithm for solution of infinite horizon ...
International audienceMarkov Decision Processes (MDP) are a widely used model including both non-det...
Partially observable Markov decision processes (POMDPs) have recently become pop-ular among many AI ...
We present a technique for speeding up the convergence of value iteration for partially observable M...
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solu...
This research focuses on Markov Decision Processes (MDP). MDP is one of the most important and chall...
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solu...