Distributed Optimization Through Deep Reinforcement Learning

Sammanfattning: Reinforcement learning methods allows self-learningagents to play video- and board games autonomously. Thisproject aims to study the efficiency of the reinforcement learningalgorithms Q-learning and deep Q-learning for dynamical multi-agent problems. The goal is to train robots to optimally navigatethrough a warehouse without colliding.A virtual environment was created, in which the learning algo-rithms were tested by simulating moving agents. The algorithms’efficiency was evaluated by how fast the agents learned to performpredetermined tasks.The results show that Q-learning excels in simple problemswith few agents, quickly solving systems with two active agents.Deep Q-learning proved to be better suited for complex systemscontaining several agents, though cases of sub-optimal movementwere still possible. Both algorithms showed great potential fortheir respective areas however improvements still need to be madefor any real-world use.