Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents

TL;DR

This paper discusses the potential and challenges of developing autonomous, intrinsically-motivated agents that explore textual environments, emphasizing their unique properties and the opportunities for advancing benchmarks in this domain.

Contribution

It highlights the synergy between text environments and autonomous agents, identifying key properties, exploration drivers, and challenges for future research.

Findings

Text worlds have properties suitable for autonomous exploration.

Autonomous agents can potentially make progress on text environment benchmarks.

Key challenges include developing effective exploration strategies in textual settings.

Abstract

In this extended abstract we discuss the opportunities and challenges of studying intrinsically-motivated agents for exploration in textual environments. We argue that there is important synergy between text environments and autonomous agents. We identify key properties of text worlds that make them suitable for exploration by autonmous agents, namely, depth, breadth, progress niches and the ease of use of language goals; we identify drivers of exploration for such agents that are implementable in text worlds. We discuss the opportunities of using autonomous agents to make progress on text environment benchmarks. Finally we list some specific challenges that need to be overcome in this area.