Initial pre-release of our C++based solver for Markov Decision Process optimization problems. The solver is based on a Modified Policy Iteration (MPI) algorithm, which derives an epsilon-optimal policy that maximizes the expected total discounted reward, where epsilon is a tolerance parameter given to the algorithm. We further provide the user with the option to choose between three different value update methods as well as switching to an epsilon-optimal Value Iteration or Policy Iteration algorithm. See the Readme-file for further information
This paper describes a novel algorithm called CONMODP for computing Pareto optimal policies for det...
We study the problem of computing the optimal value function for a Markov decision process with posi...
Dynamic programming (DP) is one of the most important mathematical programming methods. However, a m...
Markov decision processes (MDP) [1] provide a mathe-matical framework for studying a wide range of o...
The running time of the classical algorithms of the Markov Decision Process (MDP) typically grows li...
This research focuses on Markov Decision Processes (MDP). MDP is one of the most important and chall...
We study the problem of computing the optimal value function for a Markov decision process with posi...
We formally verify executable algorithms for solving Markov decision processes (MDPs) in the interac...
Markov Decision Processes (MDP) are a widely used model including both non-deterministic and probabi...
This article proposes a three-timescale simulation based algorithm for solution of infinite horizon ...
Abstract: "We present a heuristic-based propagation algorithm for solving Markov decision processes ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
Abstract. Markov Decision Processes (MDP) are a widely used model including both non-deterministic a...
We study the problem of computing the optimal value function for a Markov decision process with posi...
Abstract — This paper describes a novel algorithm called CON-MODP for computing Pareto optimal polic...
This paper describes a novel algorithm called CONMODP for computing Pareto optimal policies for det...
We study the problem of computing the optimal value function for a Markov decision process with posi...
Dynamic programming (DP) is one of the most important mathematical programming methods. However, a m...
Markov decision processes (MDP) [1] provide a mathe-matical framework for studying a wide range of o...
The running time of the classical algorithms of the Markov Decision Process (MDP) typically grows li...
This research focuses on Markov Decision Processes (MDP). MDP is one of the most important and chall...
We study the problem of computing the optimal value function for a Markov decision process with posi...
We formally verify executable algorithms for solving Markov decision processes (MDPs) in the interac...
Markov Decision Processes (MDP) are a widely used model including both non-deterministic and probabi...
This article proposes a three-timescale simulation based algorithm for solution of infinite horizon ...
Abstract: "We present a heuristic-based propagation algorithm for solving Markov decision processes ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
Abstract. Markov Decision Processes (MDP) are a widely used model including both non-deterministic a...
We study the problem of computing the optimal value function for a Markov decision process with posi...
Abstract — This paper describes a novel algorithm called CON-MODP for computing Pareto optimal polic...
This paper describes a novel algorithm called CONMODP for computing Pareto optimal policies for det...
We study the problem of computing the optimal value function for a Markov decision process with posi...
Dynamic programming (DP) is one of the most important mathematical programming methods. However, a m...