Exploring Exploration: Comparing Children with RL Agents in Unified
  Environments
  Research in developmental psychology consistently shows that children explore
the world thoroughly and efficiently and that this exploration allows them to
learn. In turn, this early learning supports more robust generalization and
intelligent behavior later in life. While much work has gone into developing
methods for exploration in machine learning, artificial agents have not yet
reached the high standard set by their human counterparts. In this work we
propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly
compare child and agent behaviors and to develop new exploration techniques. We
outline two ongoing experiments to demonstrate the effectiveness of a direct
comparison, and outline a number of open research questions that we believe can
be tested using this methodology.
