Causal-aware Safe Policy Improvement for Task-oriented dialogue

Understand

The recent success of reinforcement learning's (RL) in solving complex tasks is most often attributed to its capacity to explore and exploit an environment where it has been trained.

• Sample efficiency is usually not an issue since cheap simulators are available to sample data on-policy.
• On the other hand, task oriented dialogues are usually learnt from offline data collected using human demonstrations.
• Collecting diverse demonstrations and annotating them is expensive.