Multi-agent systems are emerging as a crucial element in our pursuit of designing and building intelligent systems. In order to succeed in the real world artificial agents must be able to cooperate, communicate, and reason about other agents’ beliefs, intentions and behaviours. Furthermore, as system designers we need to think about composing intelligent systems from intelligent subsystems, a multi-agent approach inspired by the observation that intelligent agents like organisations or governments are composed of other agents. Last but not least, as a product of evolution intelligence did not emerge in isolation, but as a group phenomenon. Hence, it seems plausible that learning agents require interaction with other agents to develop intelligence.
In this talk, I will discuss the exciting role that deep multi-agent reinforcement learning can play in the design and training of intelligent agents. In particular, training RL agents in interaction with each other can lead to the emergence of an automatic learning curriculum: From the perspective of each learning agent, the evolving behaviours of the other learning agents constitute a challenging environment dynamics and pose ever evolving tasks. I will present three case studies of deep multi-agent RL with auto-curricula: i) Learning to play board games at master level with AlphaZero, ii) Learning to play the game of Capture-The-Flag in 3d environments, and iii) Learning to cooperate in social dilemmas.