One of the core challenges in Visual Dialogue problems is asking the question
that will provide the most useful information towards achieving the required
objective. Encouraging an agent to ask the right questions is difficult because
we don't know a-priori what information the agent will need to achieve its
task, and we don't have an explicit model of what it knows already. We propose
a solution to this problem based on a Bayesian model of the uncertainty in the
implicit model maintained by the visual dialogue agent, and in the function
used to select an appropriate output. By selecting the question that minimises
the predicted regret with respect to this implicit model the agent actively
reduces ambiguity. The Bayesian model of uncertainty also enables a principled
method for identifying when enough information has been acquired, and an action
should be selected. We evaluate our approach on two goal-oriented dialogue
datasets, one for visual-based collaboration task and the other for a
negotiation-based task. Our uncertainty-aware information-seeking model
outperforms its counterparts in these two challenging problems.

本篇论文提出了基于贝叶斯模型和不确定性感知的信息寻求模型，以解决视觉对话问题中 agent 提出问题的困难。实验结果表明，在两种挑战性的问题中，我们的方法都优于其它相应的方法。

如何提问？以不确定性为指导的目标导向问题

What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions

Model-based reinforcement learning (RL) algorithms can attain excellent
sample efficiency, but often lag behind the best model-free algorithms in terms
of asymptotic performance. This is especially true with high-capacity
parametric function approximators, such as deep networks. In this paper, we
study how to bridge this gap, by employing uncertainty-aware dynamics models.
We propose a new algorithm called probabilistic ensembles with trajectory
sampling (PETS) that combines uncertainty-aware deep network dynamics models
with sampling-based uncertainty propagation. Our comparison to state-of-the-art
model-based and model-free deep RL algorithms shows that our approach matches
the asymptotic performance of model-free algorithms on several challenging
benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125
times fewer samples than Soft Actor Critic and Proximal Policy Optimization
respectively on the half-cheetah task).

本研究旨在利用基于不确定性的深度网络动态模型来提高回报函数学习算法的样本效率，并通过样本传播方法实现不确定性处理，从而解决参数化函数逼近器，如深度网络的性能下降问题，我们提出了一种名为 PETS 的新算法。与深度强化学习的先进算法进行比较，结果表明我们的方法可以在 Asymptotic Performance 上与模型自由算法匹配，并且在许多具有挑战性的基准任务中需要明显较少的样本数量（例如，在半猎豹任务中所需样本数量比 Soft Actor Critic 和 Proximal Policy Optimization 分别减少 8 倍和 125 倍）。