On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems

Understand

The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning.

• In real-world applications, using explicit user feedback as the reward signal is often unreliable and costly to collect.
• This problem can be mitigated if the user's intent is known in advance or data is available to pre-train a task success predictor off-line.
• In practice neither of these apply for most real world applications.