A REINFORCEMENT LEARNING APPROACH FOR MULTIAGENT NAVIGATION

This paper presents a Q-Learning-based multiagent system oriented to provide navigation skills to simulation agents in virtual environments. We focus on learning local navigation behaviours from the interactions with other agents and the environment. We adopt an environment-independent state space representation to provide the required scalability of such kind of systems. In this way, we evaluate whether the learned action-value functions can be transferred to other agents to increase the size of the group without loosing behavioural quality. We explain the learning process defined and the the results of the collective behaviours obtained in a well-known experiment in multiagent navigation: the exit of a place through a door.