Everett

Humans, like all animals, both cooperate and compete with each other. Through these interactions we learn to observe, act, and manipulate to maximise our utility function, and continue doing so as others learn with us. This is a decentralised non-stationary learning problem, where to survive and flourish an agent must adapt to the gradual changes of other agents as they learn, as well as capitalise on sudden shifts in their behaviour. To learn in the presence of such non-stationarity, we introduce the Switching Agent Model (SAM) that combines traditional deep reinforcement learning – which typically performs poorly in such settings – with opponent modelling, using uncertainty estimations to robustly switch between multiple policies. We empirically show the success of our approach in a multi-agent continuous-action environment, demonstrating SAM's ability to identify, track, and adapt to gradual and sudden changes in the behaviour of non-stationary agents.