Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments

Intelligence, which leverages sociocultural interactions to enhance open-ended skill acquisition. Artificial Intelligence (AI) aims to create autonomous agents that can operate across diverse environments and complete a wide range of tasks. Researchers pursue different approaches, each focusing on specific drivers of learning. In Reinforcement Learning (RL) [1], agents learn by exploring their environment and using their experience to solve tasks. Imitation Learning (IL) [2] involves agents learning from expert demonstrations, while Multi-Agent Reinforcement Learning (MARL) [3] emphasizes cooperation among agents to solve collaborative tasks. Recent advancements in RL have demonstrated success in varied domains, such as playing Atari games [4], mastering chess and Go [5], and controlling stratospheric balloons [6]. IL, combined with transformers [7], has enabled generalist agents to be trained on diverse datasets and to perform in-context reinforcement learning via algorithm distillation. However, these algorithms remain sample-inefficient and struggle with generalization, creativity, and tackling novel tasks, largely because they rely on isolated learning signals. This research explores sociocultural interactions as a new avenue for AI learning inspired by human development.