This paper presents a new algorithm for enhancing the efficiency of simulation-based optimisation using local search and neural network metamodels. The local search strategy is ba...
The problem of reinforcement learning in large factored Markov decision processes is explored. The Q-value of a state-action pair is approximated by the free energy of a product o...
Abstract. Minimizing the makespan or maximizing the minimum machine load are two of the most important and fundamental parallel machine scheduling problems. In an online scenario, ...
A recent lifting technique led to a computationally efficient Model Predictive Control (MPC) strategy in which the online optimization is performed using a univariate Newton-Raphs...
It is well known that stochastic control systems can be viewed as Markov decision processes (MDPs) with continuous state spaces. In this paper, we propose to apply the policy iter...