Get Started
Research questionHow can learned world models prevent policies from exploiting prediction errors and failing in the real world?RL policies can exploit small prediction errors in learned simulators, producing strong simulated performance that fails to transfer to reality. The difficulty is ensuring simulator accuracy where policy decisions make errors consequential.
Machine Learning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator LearningThe source provides theoretical analysis and an active data-selection algorithm for policy-aware simulator learning, with experiments on continuous-control tasks. Its evidence covers simulator-trained policies and reported real-world performance; it does not establish results for broader environments or deployment conditions.research paper · Sep 2, 2026
Related questions
How can world-model reinforcement learning produce reliable long-horizon driving policies amid interactive traffic and diverse driving styles?How can object-centric world models predict manipulation-induced changes without accumulating errors that derail planning?How can world models guide safe intervention in embodied systems when likely futures omit consequences and uncertainty?How can robots safely learn dynamic manipulation skills online despite sim-to-real mismatch?
Home
Topics
Search
Library