Distributed Deep Reinforcement Learning for a Multi-Robot Warehouse System

Sammanfattning: This project concerns optimizing the behavior ofmultiple dispatching robots in a virtual warehouse environment.Q-learning and deep Q-learning algorithms, two establishedmethods in reinforcement learning, were used for this purpose.Simulations were run during the project, implementing andcomparing different algorithms on environments with up to fourrobots. The efficiency of a given algorithm was assessed primarilyby the number of packages it enabled the robots to deliver andhow fast the solution converged. The simulation results revealedthat a Q-learning algorithm could solve problems in environmentswith up to two active robots efficiently. To solve more complexproblems in environments with more than two robots, deep Qlearninghad to be implemented to avoid prolonged computationsand excessive memory usage.