Latent transitions in models like Dreamer are biased toward dense regions, creating attractors that hide true dynamics discrepancies and cause epistemic uncertainty to be unreliable while overestimating rewards.
Dynamic horizon value estimation for model-based reinforcement learning
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Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Space Models
Latent transitions in models like Dreamer are biased toward dense regions, creating attractors that hide true dynamics discrepancies and cause epistemic uncertainty to be unreliable while overestimating rewards.