Reviews: Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems

Paper: 5353 This paper discusses representation learning problem, and designs an algorithm to learn variational parameters of approximate inference. The paper has adopted a semi-amortized technique for learning variational parameters of dynamic system for temporal data. In this paper, dynamic system is generated by state space model. In order to express the state space model, just initial state and control input at every time steps needs to be inferred. Paper has utilized adaptive path-integral technique for variational parameter refinement of dynamic system to mitigate the amortization gap( induced by limitation of inference network parameters compared to optimal variational distribution).