In the quest for autonomous agents learning open-ended repertoires of skills,
most works take a Piagetian perspective: learning trajectories are the results
of interactions between developmental agents and their physical environment.
The Vygotskian perspective, on the other hand, emphasizes the centrality of the
socio-cultural environment: higher cognitive functions emerge from
transmissions of socio-cultural processes internalized by the agent. This paper
argues that both perspectives could be coupled within the learning of autotelic
agents to foster their skill acquisition. To this end, we make two
contributions: 1) a novel social interaction protocol called Help Me Explore
(HME), where autotelic agents can benefit from both individual and socially
guided exploration. In social episodes, a social partner suggests goals at the
frontier of the learning agent knowledge. In autotelic episodes, agents can
either learn to master their own discovered goals or autonomously rehearse
failed social goals; 2) GANGSTR, a graph-based autotelic agent for manipulation
domains capable of decomposing goals into sequences of intermediate sub-goals.
We show that when learning within HME, GANGSTR overcomes its individual
learning limits by mastering the most complex configurations (e.g. stacks of 5
blocks) with only few social interventions.

本文探讨了自主学习代理程序开放式技能库的学习，提出了结合 Piaget 视角和 Vygotsky 视角的方法，通过提供新的社交交互协议 Help Me Explore 和基于图的自主代理程序 GANGSTR，实现了代理程序学习掌握更复杂的任务目标。