  We consider a team of reinforcement learning agents that concurrently operate
in a common environment, and we develop an approach to efficient coordinated
exploration that is suitable for problems of practical scale. Our approach
builds on seed sampling (Dimakopoulou and Van Roy, 2018) and randomized value
function learning (Osband et al., 2016). We demonstrate that, for simple
tabular contexts, the approach is competitive with previously proposed tabular
model learning methods (Dimakopoulou and Van Roy, 2018). With a
higher-dimensional problem and a neural network value function representation,
the approach learns quickly with far fewer agents than alternative exploration
schemes.
