Distributed Optimisation in Multi-Agent Systems Through Deep Reinforcement Learning

Sammanfattning: The increased availability of computing power have made reinforcement learning a popular field of science in the most recent years. Recently, reinforcement learning has been used in applications like decreasing energy consumption in data centers, diagnosing patients in medical care and in text-tospeech software. This project investigates how well two different reinforcement learning algorithms, Q-learning and deep Qlearning, can be used as a high-level planner for controlling robots inside a warehouse. A virtual warehouse was created, and the two different algorithms were tested. The reliability of both algorithms where found to be insufficient for real world applications but the deep Q-learning algorithm showed great potential and further research is encouraged.