RESEARCH PAPERYear 2025
Mastering diverse control tasks through world models
Classification
View four quadrants- Major category
- Foundational work
- Quadrant
- Not applicable
- Architecture
- Not applicable
- Prediction paradigm
- Not applicable
- Subcategories
- Classical world models & model-based RL
- Source review status
- Verified from primary sources
Category review. DreamerV3 develops broadly reusable latent model-based reinforcement learning across diverse control domains. Distinct dynamics and actor/critic modules establish the classical imagination-based policy-learning thread preceding current WAMs. Reading evidence
Contribution
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Abstract
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