  Bounded rationality investigates utility-optimizing decision-makers with
limited information-processing power. In particular, information theoretic
bounded rationality models formalize resource constraints abstractly in terms
of relative Shannon information, namely the Kullback-Leibler Divergence between
the agents' prior and posterior policy. Between prior and posterior lies an
anytime deliberation process that can be instantiated by sample-based
evaluations of the utility function through Markov Chain Monte Carlo (MCMC)
optimization. The most simple model assumes a fixed prior and can relate
abstract information-theoretic processing costs to the number of sample
evaluations. However, more advanced models would also address the question of
learning, that is how the prior is adapted over time such that generated prior
proposals become more efficient. In this work we investigate generative neural
networks as priors that are optimized concurrently with anytime sample-based
decision-making processes such as MCMC. We evaluate this approach on toy
examples.
