Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management

Understand

Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation.

• However, they suffer from a poor performance in the early stages of learning.
• This is especially problematic for on-line learning with real users.
• Two approaches are introduced to tackle this problem.