The student who will present is Evertjan Peer. He conducts a joint master project for Operations Management & Logistics and Data Science in Engineering (which is a special track of Computer Science & Engineering). From the faculty IE&IS he is supervised by Yingqian Zhang. Vlado Menkovski is his first supervisor from the Computer Science faculty.
Solving the Train Unit Shunting Problem: A Deep Reinforcement Learning Approach.
The Train Unit Shunting Problem is a complex task which consists of planning train movements and cleaning/maintenance tasks on shunting yards. Current solution techniques fall short by either having a long runtime (linear programs), or producing non-intuitive solutions (local search). In this thesis I investigate whether recent successes of Deep Reinforcement Learning in solving the game of Go, and playing Atari games, can be brought to this real life planning problem. Could a Deep Reinforcement Learning agent build up experience about what good moves are when handling trains on the shunting yard? An iterative procedure is followed in which the problem formulation complexity is gradually increased. I will introduce Deep Reinforcement Learning (more specifically the Deep Q-Network) and discuss my experiences so far in applying these techniques to planning problems.
You are kindly invited to this presentation!