Multi-Agent Model-Based Reinforcement Learning Experiments in the Pursuit Evasion Game

This paper describes multi-agent learning experiments performed on tactical sequences of the pursuit evasion game on very small grids. It underlines the performance difference between a centralized approach and a distributed approach when using Rmax, a model-based reinforcement learning algorithm. The prey’s goal is to go out of the grid and the predators’ goal is to kill the prey. The prey may learn or not. The predators learn in two ways: in the centralized approach they are part of one single learning agent, and in the distributed approach, each predator is a learning agent in itself. Every agents learn to accomplish its goal by using Rmax. Our results compare the centralized approach with the distributed approach. Future works mainly include scaling up to larger boards using model-free algorithms, and exploring partial observability of agents.