Infants learn actively in their environments, shaping their own learning
curricula. They learn about their environments' affordances, that is, how local
circumstances determine how their behavior can affect the environment. Here we
model this type of behavior by means of a deep learning architecture. The
architecture mediates between global cognitive map exploration and local
affordance learning. Inference processes actively move the simulated agent
towards regions where they expect affordance-related knowledge gain. We
contrast three measures of uncertainty to guide this exploration: predicted
uncertainty of a model, standard deviation between the means of several models
(SD), and the Jensen-Shannon Divergence (JSD) between several models. We show
that the first measure gets fooled by aleatoric uncertainty inherent in the
environment, while the two other measures focus learning on epistemic
uncertainty. JSD exhibits the most balanced exploration strategy. From a
computational perspective, our model suggests three key ingredients for
coordinating the active generation of learning curricula: (1) Navigation
behavior needs to be coordinated with local motor behavior for enabling active
affordance learning. (2) Affordances need to be encoded locally for acquiring
generalized knowledge. (3) Effective active affordance learning mechanisms
should use density comparison techniques for estimating expected knowledge
gain. Future work may seek collaborations with developmental psychology to
model active play in children in more realistic scenarios.

婴儿在环境中积极学习，塑造自己的学习课程。研究通过深度学习架构模拟这种行为，结合全局认知地图探索和局部可用性学习，使用不确定性度量来指导学习，展现了最为平衡的探索策略，并提出了协调积极生成学习课程的三个关键要素。