Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents

Laetitia Teodorescu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer

Keywords: Text worlds, exploration, reinforcement learning, language-conditioned agents, autonomous agents

TL;DR: An extended abstract describing the benefits of studying exploration in textworlds with instrinsically-motivated autonomous agents

Abstract: The purpose of this extended abstract is to discuss the possible fruitful interactions between intrinsically-motivated language-conditioned agents and textual environments. We define autotelic agents as agents able to set their own goals. We identify desirable properties of textual nenvironments that makes them a good testbed for autotelic agents. We them list drivers of exploration for such agents that would allow them to achieve large repertoires of skills in these environments, enabling such agents to be repurposed for solving the benchmarks implemented in textual environments. We then discuss challenges and further perspectives brought about by this interaction.

Archival: Archival