Imitation learning, which learns agent policy by mimicking expert
demonstration, has shown promising results in many applications such as medical
treatment regimes and self-driving vehicles. However, it remains a difficult
task to interpret control policies learned by the agent. Difficulties mainly
come from two aspects: 1) agents in imitation learning are usually implemented
as deep neural networks, which are black-box models and lack interpretability;
2) the latent causal mechanism behind agents' decisions may vary along the
trajectory, rather than staying static throughout time steps. To increase
transparency and offer better interpretability of the neural agent, we propose
to expose its captured knowledge in the form of a directed acyclic causal
graph, with nodes being action and state variables and edges denoting the
causal relations behind predictions. Furthermore, we design this causal
discovery process to be state-dependent, enabling it to model the dynamics in
latent causal graphs. Concretely, we conduct causal discovery from the
perspective of Granger causality and propose a self-explainable imitation
learning framework, {\method}. The proposed framework is composed of three
parts: a dynamic causal discovery module, a causality encoding module, and a
prediction module, and is trained in an end-to-end manner. After the model is
learned, we can obtain causal relations among states and action variables
behind its decisions, exposing policies learned by it. Experimental results on
both synthetic and real-world datasets demonstrate the effectiveness of the
proposed {\method} in learning the dynamic causal graphs for understanding the
decision-making of imitation learning meanwhile maintaining high prediction
accuracy.

通过曝光由动态因果图表示的知识，以增加神经代理的可解释性并提供更好的解释能力，我们设计了一种自解释的模仿学习框架，能够学习动态因果图以理解模仿学习的决策过程并保持高预测准确性。

可解释的模仿学习的动态有向无环图发现

Dynamic DAG Discovery for Interpretable Imitation Learning

We deal with the navigation problem where the agent follows natural language
instructions while observing the environment. Focusing on language
understanding, we show the importance of spatial semantics in grounding
navigation instructions into visual perceptions. We propose a neural agent that
uses the elements of spatial configurations and investigate their influence on
the navigation agent's reasoning ability. Moreover, we model the sequential
execution order and align visual objects with spatial configurations in the
instruction. Our neural agent improves strong baselines on the seen
environments and shows competitive performance on the unseen environments.
Additionally, the experimental results demonstrate that explicit modeling of
spatial semantic elements in the instructions can improve the grounding and
spatial reasoning of the model.

我们针对导航问题进行研究，提出了一种使用空间语义元素的神经代理导航模型，并探究了它们对导航代理的推理能力的影响。研究结果表明，指令中显式建模空间语义元素可以改善模型的基础和空间推理能力，并在已见及未见环境上取得了竞争性的性能。