On the road from Model-Based Dynamical Programming to Model-Free Reinforcement Learning: A sample efficient approach

This thesis introduces a new method, called Mixed Iteration, for controlling Markov Decision Processes when partial information is known about the dynamics of the Markov Decision Process. The algorithm uses sampling to calculate the expectation of partially known dynamics in stochastic environments. Its goal is to lower the number of iterations...

Dynamic Programming based basicity control of an experimental smelting furnace prototype

This thesis discusses the chemical composition (basicity) control problem of HIsarna, an experimental iron furnace which operates with 30% less CO2 emissions than its traditional blast furnace counterparts. The control challenge is keeping the basicity of the plant in a narrow operating region. A mass balance model of the plant was constructed -...