Developmental machine learning studies how artificial agents can model the way children learn open-ended repertoires of skills. Such agents need to create and represent goals, select which ones to pursue and learn to achieve them. Recent approaches have considered goal spaces that were either fixed and hand-defined or learned using generative models of states. This limited agents to sample goals within the distribution of known effects. We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.

Additional Feedback: Line-by-line comments: Figure 1 - This figure is quite cluttered. I would recommend removing/simplifying some of the graphics (e.g. the thought bubbles) and, when possible, moving them outside the environment canvas. Line 37 - Do the citations on lines 38-39 substantiate the preceding claim that language actually influences children's exploration behavior? This seems like a very difficult claim to test, how do you disentangle mental maturity with language acquisition? Note that I do not consider children "narrating their ongoing activities" to be a meaningful change in behavior if it is not accompanied by a change in how the children actually complete those activities.

Reviewers agreed that the paper proposes an interesting model for learning language conditioned goal reaching policies and performs a thorough investigation on a simple task. There was agreement that the environment studied in the paper is quite simplistic and that the paper would benefit from a task with a richer grammar/goal space. Nevertheless, the results on the task in the paper are sufficiently interesting for acceptance as a poster.

Developmental machine learning studies how artificial agents can model the way children learn open-ended repertoires of skills. Such agents need to create and represent goals, select which ones to pursue and learn to achieve them. Recent approaches have considered goal spaces that were either fixed and hand-defined or learned using generative models of states. This limited agents to sample goals within the distribution of known effects. We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.