TY - THES A1 - Samuel Varghese, Bitty T1 - Development of a Self Learning Agent to control a Gas Network Simulator N2 - Natural gas is an indispensable source of energy for industrial and domestic purposes, hence distribution and trade demands competition. In gas distribution, optimizing is a crucial task involving control decision on the network elements with respect to trader’s demand at the boundary nodes. The application of artificial intelligence is becoming popular in diverse domains, making its role in gas transport networks more and more interesting. The opposite interest of the players in the network is the reason behind the thought of establishing a game analogy here. The agents as players act on the basis of the opponent’s action. Since both agents perform actions from different space, a neural network model is designed for the dispatcher agent to learn to make decisions for active elements based on its experience from the sample gas transport network. Behavior of the network and its elements is depicted using an optimized gas network simulator. The representation of the gas transport network and its state is digitally transformed so that it is able to extract the features by an artificial neural network representing the environment and its rules. With the help of the trained neural network, the dispatcher agent decides the control decisions with less accumulated penalties. The results and findings of the proposed method are subjected to a comparison with the interval halving method. The results of the study will open the door for further research possibilities. KW - Gas transport network simulator KW - Trader agent KW - Dispatcher agent KW - Deep reinforcement learning KW - Interval halving KW - Monte-Carlo tree search Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-8025 ER -