Causal dynamics models (CDMs) have demonstrated significant potential in
addressing various challenges in reinforcement learning. To learn CDMs, recent
studies have performed causal discovery to capture the causal dependencies
among environmental variables. However, the learning of CDMs is still confined
to small-scale environments due to computational complexity and sample
efficiency constraints. This paper aims to extend CDMs to large-scale
object-oriented environments, which consist of a multitude of objects
classified into different categories. We introduce the Object-Oriented CDM
(OOCDM) that shares causalities and parameters among objects belonging to the
same class. Furthermore, we propose a learning method for OOCDM that enables it
to adapt to a varying number of objects. Experiments on large-scale tasks
indicate that OOCDM outperforms existing CDMs in terms of causal discovery,
prediction accuracy, generalization, and computational efficiency.

本文介绍了一种新的基于对象的因果动力模型 (Object-Oriented CDM, OOCDM)，它能够在大规模对象导向的环境中学习，并通过共享因果关系和参数来适应不同类别的对象。实验证明，在因果发现、预测准确性、泛化性和计算效率方面，OOCDM 优于现有的 CDMs。

在面向对象环境中学习因果动力模型

Learning Causal Dynamics Models in Object-Oriented Environments

Current approaches for collision avoidance and space traffic management face
many challenges, mainly due to the continuous increase in the number of objects
in orbit and the lack of scalable and automated solutions. To avoid
catastrophic incidents, satellite owners/operators must be aware of their
assets' collision risk to decide whether a collision avoidance manoeuvre needs
to be performed. This process is typically executed through the use of warnings
issued in the form of CDMs which contain information about the event, such as
the expected TCA and the probability of collision. Our previous work presented
a statistical learning model that allowed us to answer two important questions:
(1) Will any new conjunctions be issued in the next specified time interval?
(2) When and with what uncertainty will the next CDM arrive? However, the model
was based on an empirical Bayes homogeneous Poisson process, which assumes that
the arrival rates of CDMs are constant over time. In fact, the rate at which
the CDMs are issued depends on the behaviour of the objects as well as on the
screening process performed by third parties. Thus, in this work, we extend the
previous study and propose a Bayesian non-homogeneous Poisson process
implemented with high precision using a Probabilistic Programming Language to
fully describe the underlying phenomena. We compare the proposed solution with
a baseline model to demonstrate the added value of our approach. The results
show that this problem can be successfully modelled by our Bayesian
non-homogeneous Poisson Process with greater accuracy, contributing to the
development of automated collision avoidance systems and helping operators
react timely but sparingly with satellite manoeuvres.

通过贝叶斯非齐次泊松过程，本研究提出了一种高精度的概率编程语言实现模型，有效地描述了碰撞危险性，并成功地模拟了自动化避碰系统的发展，有助于卫星操作者实时但谨慎地进行卫星机动操作。