  This paper presents a new approach that extends Deep Dyna-Q (DDQ) by
incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed,
small amount of user interactions (budget) for learning task-oriented dialogue
agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget
over different stages of training; (2) a controller to decide at each training
step whether the agent is trained using real or simulated experiences; (3) a
user goal sampling module to generate the experiences that are most effective
for policy learning. Experiments on a movie-ticket booking task with simulated
and real users show that our approach leads to significant improvements in
success rate over the state-of-the-art baselines given the fixed budget.
