  In humans, perceptual awareness facilitates the fast recognition and
extraction of information from sensory input. This awareness largely depends on
how the human agent interacts with the environment. In this work, we propose
active neural generative coding, a computational framework for learning
action-driven generative models without backpropagation of errors (backprop) in
dynamic environments. Specifically, we develop an intelligent agent that
operates even with sparse rewards, drawing inspiration from the cognitive
theory of planning as inference. We demonstrate on several simple control
problems that our framework performs competitively with deep Q-learning. The
robust performance of our agent offers promising evidence that a backprop-free
approach for neural inference and learning can drive goal-directed behavior.
