Using Reinforcement Learning to Coordinate Better

This paper examines the potential and the impact of introducing learning capabilities into autonomous agents that make decisions at run-time about which mechanism to exploit in order to coordinate their activities. Specifically, our motivating hypothesis is that to deal with dynamic and unpredictable environments it is important to have agents that learn the right situations in which to attempt coordination and the right coordination method to use in those situations. In particular, the efficacy of learning is evaluated when agents have varying types and amounts of information when those coordinating decisions are taken. This hypothesis is evaluated empirically, in a grid-world scenario in which a) an agent’s predictions about the other agents in the environment are approximately correct and b) an agent cannot correctly predict the others’ behaviour. The results presented show when, where and why learning is effective when it comes to making a decision about selecting a coordination mechanism.