Learning with whom to communicate using relational reinforcement learning

Relational reinforcement learning is a promising direction within reinforcement learning research. It upgrades reinforcement learning techniques by using relational representations for states, actions, and learned value-functions or policies to allow natural representations and abstractions of complex tasks. Multi-agent systems are characterized by their relational structure and present a good example of a complex task. In this paper, we show how relational reinforcement learning could be a useful tool for learning in multi-agent systems.We study this approach in more detail on one important aspect of multi-agent systems, i.e., on learning a communication policy for cooperative systems (e.g., resource distribution). Communication between agents in realistic multi-agent systems can be assumed costly, limited and unreliable. We perform a number of experiments that highlight the conditions in which relational representations can be beneficial when taking the constraints mentioned above into account.