Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents
Ahmed Akakzia, Olivier Serris, Olivier Sigaud, C\'edric Colas

TL;DR
This paper introduces a social interaction protocol called Help Me Explore (HME) and a graph-based autotelic agent, GANGSTR, demonstrating that social guidance significantly enhances skill acquisition in manipulation tasks.
Contribution
It proposes a novel social learning framework combining Piagetian and Vygotskian perspectives and develops GANGSTR, a new graph-based agent capable of complex goal decomposition.
Findings
GANGSTR masters complex configurations with few social interventions.
HME improves learning efficiency in manipulation tasks.
Social guidance extends the agent's capabilities beyond individual learning limits.
Abstract
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…
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Taxonomy
TopicsReinforcement Learning in Robotics
