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In Model-Based Reinforcement Learning (MBRL), incorporating causal structures into dynamics models provides agents with a structured understanding of the environments, enabling efficient decision.
Optimization of computer simulation models with rare events
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Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Learning invariant representations for reinforcement learning without reconstruction
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Learning dynamic attribute-factored world models for efficient multi-object reinforcement learning
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Mastering diverse domains through world models
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Quantized local independence discovery for fine-grained causal dynamics learning in reinforcement learning
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Invariant causal representation learning for out-of-distribution generalization
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Temporal predictive coding for model-based planning in latent space
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Task-independent causal state abstraction
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A consciousness-inspired planning agent for model-based reinforcement learning
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