Contrastive Learning of Structured World Models
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network. This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process. We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation. Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.
The construction and learning of structured world models is an interesting area of research that could in principle enable better generalisation and interpretability for predictive models. The authors overcome the problem of using pixel-based losses (a common issue being reconstruction of small but potentially important objects) by using a contrastive latent space. The model otherwise makes use of a fixed number of object slots and a GNN transition model, similarly to prior approaches. The authors back up their method with nice results on 3D cubes and 3-body physics domains, and reasonable initial results on two Atari games, with ablations on the different components showing their contributions, so I would give this paper an accept. The comparisons to existing literature and related areas is very extensive, with interesting pointers to potential future work - particularly on the transition model and graph embeddings. As expected, the object-factorized action space appears to work well for generalisation, and could be extended/adapted, but setting a fixed number of objects K is a clearly fundamentally limiting hyperparameter, and so showing how the model performs under misspecification of this hyperparameter is useful to know for settings where this is known (2D shapes, 3D blocks, 3-body physics). The fact that K=1 is the best for Pong but K=5 is the best for Space Invaders raises at least two questions: can scaling K > 5 further improve performance on Space Invaders, and is it possible to make the model more robust to a greater-than-needed number of object slots? On a similar note, the data collection procedure for the Atari games seems to indicate that the model is quite sensitive to domains where actions rarely have an impact on the transition dynamics, or the interaction is more complex (e.g. other agents exist in the world) - coming up with a synthetic dataset where the importance of this can be quantified would again aid understanding of the authors* proposed method.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Contrastive Learning of Structured World Models
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network. This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process. We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation. Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The construction and learning of structured world models is an interesting area of research that could in principle enable better generalisation and interpretability for predictive models. The authors overcome the problem of using pixel-based losses (a common issue being reconstruction of small but potentially important objects) by using a contrastive latent space. The model otherwise makes use of a fixed number of object slots and a GNN transition model, similarly to prior approaches. The authors back up their method with nice results on 3D cubes and 3-body physics domains, and reasonable initial results on two Atari games, with ablations on the different components showing their contributions, so I would give this paper an accept. The comparisons to existing literature and related areas is very extensive, with interesting pointers to potential future work - particularly on the transition model and graph embeddings. As expected, the object-factorized action space appears to work well for generalisation, and could be extended/adapted, but setting a fixed number of objects K is a clearly fundamentally limiting hyperparameter, and so showing how the model performs under misspecification of this hyperparameter is useful to know for settings where this is known (2D shapes, 3D blocks, 3-body physics). The fact that K=1 is the best for Pong but K=5 is the best for Space Invaders raises at least two questions: can scaling K > 5 further improve performance on Space Invaders, and is it possible to make the model more robust to a greater-than-needed number of object slots? On a similar note, the data collection procedure for the Atari games seems to indicate that the model is quite sensitive to domains where actions rarely have an impact on the transition dynamics, or the interaction is more complex (e.g. other agents exist in the world) - coming up with a synthetic dataset where the importance of this can be quantified would again aid understanding of the authors* proposed method.
assistant
The construction and learning of structured world models is an interesting area of research that could in principle enable better generalisation and interpretability for predictive models. The authors overcome the problem of using pixel-based losses (a common issue being reconstruction of small but potentially important objects) by using a contrastive latent space. The model otherwise makes use of a fixed number of object slots and a GNN transition model, similarly to prior approaches. The authors back up their method with nice results on 3D cubes and 3-body physics domains, and reasonable initial results on two Atari games, with ablations on the different components showing their contributions, so I would give this paper an accept. The comparisons to existing literature and related areas is very extensive, with interesting pointers to potential future work - particularly on the transition model and graph embeddings. As expected, the object-factorized action space appears to work well for generalisation, and could be extended/adapted, but setting a fixed number of objects K is a clearly fundamentally limiting hyperparameter, and so showing how the model performs under misspecification of this hyperparameter is useful to know for settings where this is known (2D shapes, 3D blocks, 3-body physics). The fact that K=1 is the best for Pong but K=5 is the best for Space Invaders raises at least two questions: can scaling K > 5 further improve performance on Space Invaders, and is it possible to make the model more robust to a greater-than-needed number of object slots? On a similar note, the data collection procedure for the Atari games seems to indicate that the model is quite sensitive to domains where actions rarely have an impact on the transition dynamics, or the interaction is more complex (e.g. other agents exist in the world) - coming up with a synthetic dataset where the importance of this can be quantified would again aid understanding of the authors* proposed method.