Reinforcement learning for planning of a simulated production line
Deep reinforcement learning has been shown to be able to solve tasks without prior knowledge of thedynamics of the problems. In this thesis the applicability of reinforcement learning on the problem ofproduction planing is evaluated. Experiments are performed in order to reveal strengths and weak-nesses of the theory currently available. Reinforcement learning shows great potential but currentlyon
