2026
WIMLE: Uncertainty‑Aware World Models with IMLE for Sample‑Efficient Continuous Control
ICLR 2026poster
Model-based reinforcement learning promises strong sample efficiency but often underperforms in practice due to compounding model error, unimodal world models that average over multi-modal dynamics, and overconfident predictions that bias learning. We introduce WIMLE, a model-based method that exten…