  Reinforcement learning is widely used for dialogue policy optimization where
the reward function often consists of more than one component, e.g., the
dialogue success and the dialogue length. In this work, we propose a structured
method for finding a good balance between these components by searching for the
optimal reward component weighting. To render this search feasible, we use
multi-objective reinforcement learning to significantly reduce the number of
training dialogues required. We apply our proposed method to find optimized
component weights for six domains and compare them to a default baseline.
