  Reinforcement learning methods have been used for learning dialogue policies.
However, learning an effective dialogue policy frequently requires
prohibitively many conversations. This is partly because of the sparse rewards
in dialogues, and the very few successful dialogues in early learning phase.
Hindsight experience replay (HER) enables learning from failures, but the
vanilla HER is inapplicable to dialogue learning due to the implicit goals. In
this work, we develop two complex HER methods providing different trade-offs
between complexity and performance, and, for the first time, enabled HER-based
dialogue policy learning. Experiments using a realistic user simulator show
that our HER methods perform better than existing experience replay methods (as
applied to deep Q-networks) in learning rate.
