Electrical Engineering and Computer Sciences, Massachusetts Institute of Technology; Center of Mathematical Sciences and Applications, Harvard University; Courant Institute of Mathematical Sciences, New York University
Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Kaelbling
model-based;reinforcement learning;end-to-end;implicit
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization