The Markov-modulated Poisson process (MMPP) cookbook
Performance Evaluation
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Scheduling Multiclass Packet Streams to Minimize Weighted Loss
Queueing Systems: Theory and Applications
Parallel Rollout for Online Solution of Partially Observable Markov Decision Processes
Discrete Event Dynamic Systems
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Parallel rollout is a formal method of combining multiple heuristic policies available to a sequential decision maker in the framework of Markov Decision Processes (MDPs). The method improves the performances of all of the heuristic policies adapting to the different stochastic system trajectories. From an inherent multi-level parallelism in the method, in this paper we propose a parallelized version of parallel rollout algorithm, and evaluate its performance on a multi-class task scheduling problem by using OpenMP and MPI programming model. We analyze and compare the performance in two versions of parallelized codes, e.g., OpenMP and MPI on several execution environment. We show that the performance using OpenMP API is higher than MPI due to lower overhead in data synchronization across processors.