  Neural network-based systems can now learn to locate the referents of words
and phrases in images, answer questions about visual scenes, and execute
symbolic instructions as first-person actors in partially-observable worlds. To
achieve this so-called grounded language learning, models must overcome
challenges that infants face when learning their first words. While it is
notable that models with no meaningful prior knowledge overcome these
obstacles, researchers currently lack a clear understanding of how they do so,
a problem that we attempt to address in this paper. For maximum control and
generality, we focus on a simple neural network-based language learning agent,
trained via policy-gradient methods, which can interpret single-word
instructions in a simulated 3D world. Whilst the goal is not to explicitly
model infant word learning, we take inspiration from experimental paradigms in
developmental psychology and apply some of these to the artificial agent,
exploring the conditions under which established human biases and learning
effects emerge. We further propose a novel method for visualising semantic
representations in the agent.
