Analytic Planning under Uncertainty
Moment-closure methods for model-based RL and planning.
This project studies analytic planning methods that propagate compact approximations of uncertainty rather than relying only on sampling. The goal is to understand when structured approximations can make planning under uncertainty more tractable while retaining useful predictive information.
Keywords: model-based reinforcement learning, planning, uncertainty, moment closure, approximate inference.