Learning Causal Dynamics Models in Object-Oriented Environments
Zhongwei Yu, Jingqing Ruan, and Dengpeng Xing
TL;DR
This paper introduces Object-Oriented Causal Dynamics Models (OOCDMs) to effectively learn causal relationships in large-scale, object-oriented environments, improving accuracy, generalization, and efficiency over existing models.
Contribution
The paper presents a novel OOCDM framework that shares causal parameters among object classes and adapts to varying object counts, extending causal modeling to complex environments.
Findings
OOCDM outperforms existing CDMs in causal discovery.
OOCDM achieves higher prediction accuracy.
OOCDM demonstrates better generalization and computational efficiency.
Abstract
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,…
Peer Reviews
Decision·ICML 2024 Poster
- The use of OO concept in causal dynamics learning seems a very well-motivated work where real-world environments are naturally multi-agent setting with heterogeneous players. - I guess the use of attention to handle a varying number of objects’ attributes seems a clever idea. (Is this idea already adopted in other OO related research?, The authors only left a figure and a single sentence before Section 4.3)
As a researcher who worked on causal discovery in relational data and causal dynamics learning, this combination seems interesting. However, - The combination seems a bit not nontrivial in a sense that, while the fomulation is a bit complicated but, at a fundamental level, we just generalize causal dynamics learning to an object-oriented version, and apply the idea of conditional independence. Causal dynamics learning or causal discovery is just a transition probability between the two time ste
**[Motivation and General Idea]** The manuscript establishes a robust motivation, addressing the critical challenge of inefficiencies prevalent in Causal Dynamic Models (CDMs) within complex reinforcement learning (RL) scenarios. The authors’ choice to navigate RL in the context of multiple objects and categories is judicious and aligns well with the overarching theme of the paper. **[Proposed Framework]** The architectural design of the proposed framework is simple to follow and technically so
I listed the weaknesses and questions here. I am also a reviewer of the previous version of this paper. Most of my concerns have been addressed by the authors. However, some of them are still a bit unclear to me. - **[Definition of the local causality]** When we typically mention local causality, we refer to the case where the causal edges vary, as illustrated in Figure 2 in [1]. In this paper, when the authors mention local causality, it refers more to the causal graph of transitions for in
(1) the authors formally defined the problem of object oriented learning in RL from a causal perspective, which paved the way for future work. (2) the authors identified the core problem of the current causal dynamics model learning algorithms, which is the poor efficiency in the face of mass variables. The proposed method alleviated this problem to some extent with intuitive explanations and thorough justifications. (3) the writing is clear and easy to follow.
(1) Dynamics bayesian networks are rather limited rendering them unsuitable to model causal mechanisms. Especially when there is a need for cross layer inference, i.e., identifying the effect of an intervention from observational data. For more information, please see "On Pearl’s hierarchy and the foundations of causal inference." in Probabilistic and Causal Inference: the works of Judea Pearl. In the specific scenario like the one define in this paper, it would be fine (Markovian, no confounder
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Taxonomy
TopicsAI-based Problem Solving and Planning · Semantic Web and Ontologies · Advanced Database Systems and Queries