Concept-Guided Spatial Regularization for World Models in Atari Pong
This paper investigates how well certain AI models can simulate the game Pong. It finds that even if these models perform well in training, they often fail when tested on their own, especially in tracking the ball's movements.
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- 1 We apply it to the image prediction or reconstruction objective of the corresponding visual learning module.
- 2 Improving the reconstruction of the ball region is not sufficient to produce a reliable policy-training simulator.
- 3 The reduction matching described above is then applied to the aggregate loss.
- 4 TWISTER [Burchi and Timofte, 2025 ] adopts a latent-space world-model architecture similar to STORM, using a Transformer-based dynamics model to predict action-conditioned future latent states.
Introduction
Their agent performance matches original reports, yet their frozen world models are unreliable. Their generated trajectories contain failures, and policies trained from scratch inside these models transfer poorly.
Many rollout failures involve the ball and its interactions.
We propose Concept-Guided Spatial Regularization (CGSReg) to add reconstruction supervision to image regions corresponding to concepts.
Improving reconstruction of the ball region cannot address the dominant error in STORM.
Research Question
We apply it to the image prediction or reconstruction objective of the corresponding visual learning module.
Methodology
These observations motivate our hypothesis that insufficient modeling of task-critical concepts contributes to frozen-model failures. The rollouts often preserve the global Pong scene but frequently break task-critical objects and interactions.
Study Design
This motivates our hypothesis that task-critical concepts are insufficiently modeled.
We use the ball as the main task-critical concept and obtain its mask with SAM2.
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Results & Findings
World models are evaluated through the reinforcement-learning performance of the complete model-based reinforcement learning (MBRL) agent. Strong agent performance does not guarantee that the learned world model is a reliable simulator.
- World models are evaluated through the reinforcement-learning performance of the complete model-based reinforcement learning (MBRL) agent.
- Strong agent performance does not guarantee that the learned world model is a reliable simulator.
- We reproduce five representative visual world-model agents in Atari Pong.
- DreamerV3’s mean Pong return falls significantly when policy learning is performed inside the frozen world model.
- Improve \u03c0 using the generated rollouts and return the trained policy or planner \u03c0 and world model M \u03d5.
Future work should generalize CGSReg and reduce its reliance on manually specified concepts.
The cause of the frozen-world-model gap remains unclear.
Practical Applications
An observation may be an image or a state describing the environment. Other factors, including robustness to policy shifts, long-horizon consistency, and action-conditioned prediction, may also contribute and remain important directions for future work.
Other factors, including robustness to policy shifts, long-horizon consistency, and action-conditioned prediction, may also contribute and remain important directions for future work.
Closed-Loop Rollout Diagnosis
The study employs a pixel-based PPO policy to probe the frozen world models, revealing recurring failure modes such as ball disappearance and incorrect interactions, which affect rewards and policy learning.
DreamerV3
Specific failures in DreamerV3 are documented, including ball disappearance and incorrect paddle interactions, illustrating the model’s limitations in simulating the game dynamics accurately.
Diamond
The section discusses the performance and failures of the DIAMOND model in the context of the study, although specific details are not provided in the excerpt.
Frequently Asked Questions
A point is scored when one side fails to return the ball. We apply it to the image prediction or reconstruction objective of the corresponding visual learning module.
These observations motivate our hypothesis that insufficient modeling of task-critical concepts contributes to frozen-model failures. The rollouts often preserve the global Pong scene but frequently break task-critical objects and interactions.
Improving the reconstruction of the ball region is not sufficient to produce a reliable policy-training simulator. The reduction matching described above is then applied to the aggregate loss.
The policy may therefore benefit from a model that is useful near its current data distribution without requiring the model to become a strong standalone simulator after freezing. This diagnostic follows each project’s native training and evaluation protocol and is therefore separate.
Future work should generalize CGSReg and reduce its reliance on manually specified concepts. The cause of the frozen-world-model gap remains unclear.
This paper investigates how well certain AI models can simulate the game Pong. It finds that even if these models perform well in training, they often fail when tested on their own, especially in tracking the ball’s movements.