Most existing approaches for goal-oriented dialogue policy learning used
reinforcement learning, which focuses on the target agent policy and simply
treat the opposite agent policy as part of the environment. While in real-world
scenarios, the behavior of an opposite agent often exhibits certain patterns or
underlies hidden policies, which can be inferred and utilized by the target
agent to facilitate its own decision making. This strategy is common in human
mental simulation by first imaging a specific action and the probable results
before really acting it. We therefore propose an opposite behavior aware
framework for policy learning in goal-oriented dialogues. We estimate the
opposite agent's policy from its behavior and use this estimation to improve
the target agent by regarding it as part of the target policy. We evaluate our
model on both cooperative and competitive dialogue tasks, showing superior
performance over state-of-the-art baselines.

提出了一种考虑对手行为的目标导向对话策略学习框架，通过对对手行为的估计来优化自身策略，实现在合作与竞争对话任务中的优越表现。