We show that the joint problem boils down to two steps:
1. Infer closed-loop parameters (which can be done efficiently with SSM methods ✅)
2. Derive equations relating the parameters of interest in setting the closed-loop dynamics.
See our paper (also on arXiv, link above) for details!
Inferring both the system dynamics *and* the control objective from partial observations is inherently ill-posed. Characterizing the exact (non-)identifiability and identifying how to perform inference was the challenge!
Assuming known environments or costs is reasonable in engineered systems, but maybe less so for intelligent agents in complex worlds.
A year later, I see this as clarifying how unobserved objectives and dynamics interact to produce a continuum of explanations, and which perturbations are needed.
