MrCoM: A Meta-Regularized World-Model Generalizing Across Multi-Scenarios Article Swipe
Xiong, Xuantang
,
Mu Ni
,
Xie, Runpeng
,
YANG Senhao
,
Wang Yaqing
,
Wang Le-xiang
,
LUAN-Yao
,
Li, Siyuan
,
Xu Shuang
,
Yang Yiqin
,
Xu, Bo
·
YOU?
·
· 2025
· Open Access
·
YOU?
·
· 2025
· Open Access
·
Model-based reinforcement learning (MBRL) is a crucial approach to enhance the generalization capabilities and improve the sample efficiency of RL algorithms. However, current MBRL methods focus primarily on building world models for single tasks and rarely address generalization across different scenarios. Building on the insight that dynamics within the same simulation engine share inherent properties, we attempt to construct a unified world model capable of generalizing across different scenarios, named Meta-Regularized Contextual World-Model (MrCoM). This method first decomposes the latent state space into various components based on the dynamic characteristics, thereby enhancing the accuracy of world-model prediction. Further, MrCoM adopts meta-state regularization to extract unified representation of scenario-relevant information, and meta-value regularization to align world-model optimization with policy learning across diverse scenario objectives. We theoretically analyze the generalization error upper bound of MrCoM in multi-scenario settings. We systematically evaluate our algorithm's generalization ability across diverse scenarios, demonstrating significantly better performance than previous state-of-the-art methods.
Related Topics
Concepts
Generalization
Regularization (linguistics)
Computer science
Artificial intelligence
Reinforcement learning
Representation (politics)
Generalization error
Machine learning
Construct (python library)
Focus (optics)
State space
Space (punctuation)
Feature learning
Simple (philosophy)
Mathematics
Sample (material)
State (computer science)
Early stopping
Latent variable
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.06252
- https://arxiv.org/pdf/2511.06252
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7105506676
All OpenAlex metadata
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https://openalex.org/W7105506676Canonical identifier for this work in OpenAlex
- Title
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MrCoM: A Meta-Regularized World-Model Generalizing Across Multi-ScenariosWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-09Full publication date if available
- Authors
-
Xiong, Xuantang, Mu Ni, Xie, Runpeng, YANG Senhao, Wang Yaqing, Wang Le-xiang, LUAN-Yao, Li, Siyuan, Xu Shuang, Yang Yiqin, Xu, BoList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.06252Publisher landing page
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-
https://arxiv.org/pdf/2511.06252Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2511.06252Direct OA link when available
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Generalization, Regularization (linguistics), Computer science, Artificial intelligence, Reinforcement learning, Representation (politics), Generalization error, Machine learning, Construct (python library), Focus (optics), State space, Space (punctuation), Feature learning, Simple (philosophy), Mathematics, Sample (material), State (computer science), Early stopping, Latent variableTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.algorithm's | 140 |
| abstract_inverted_index.algorithms. | 20 |
| abstract_inverted_index.objectives. | 122 |
| abstract_inverted_index.performance | 149 |
| abstract_inverted_index.prediction. | 96 |
| abstract_inverted_index.properties, | 54 |
| abstract_inverted_index.world-model | 95, 114 |
| abstract_inverted_index.capabilities | 12 |
| abstract_inverted_index.generalizing | 65 |
| abstract_inverted_index.information, | 108 |
| abstract_inverted_index.optimization | 115 |
| abstract_inverted_index.demonstrating | 146 |
| abstract_inverted_index.reinforcement | 1 |
| abstract_inverted_index.significantly | 147 |
| abstract_inverted_index.theoretically | 124 |
| abstract_inverted_index.generalization | 11, 37, 127, 141 |
| abstract_inverted_index.multi-scenario | 134 |
| abstract_inverted_index.regularization | 101, 111 |
| abstract_inverted_index.representation | 105 |
| abstract_inverted_index.systematically | 137 |
| abstract_inverted_index.Meta-Regularized | 70 |
| abstract_inverted_index.characteristics, | 89 |
| abstract_inverted_index.state-of-the-art | 152 |
| abstract_inverted_index.scenario-relevant | 107 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.91526236 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |