text: abstract
  we introduce a new formulation of the hidden parameter markov decision process (hip-mdp), a framework for modeling families of related tasks using lowdimensional latent embeddings. our new framework correctly models the joint
  uncertainty in the latent parameters and the state space. we also replace the original
  gaussian process-based model with a bayesian neural network, enabling more
  scalable inference. thus, we expand the scope of the hip-mdp to applications
  with higher dimensions and more complex dynamics.