A Comparative Study of Multi-Agent Reinforcement Learning on Real World Problems

This work investigates the multi-agent reinforcement learning methods that have applicability to real-world scenarios including stochastic, partially observable, and infinite horizon problems. These problems are hard due to large state and control spaces and may require some form of intelligent multi-agent behavior to achieve the target objective.