Learning Dynamic Belief Graphs to Generalize on Text-Based Games

Understand

Playing text-based games requires skills in processing natural language and sequential decision making.

• Achieving human-level performance on text-based games remains an open challenge, and prior research has largely relied on hand-crafted structured representations and heuristics.
• In this work, we investigate how an agent can plan and generalize in text-based games using graph-structured representations learned end-to-end from raw text.
• We propose a novel graph-aided transformer agent (GATA) that infers and updates latent belief graphs during planning to enable effective action selection by capturing the underlying game dynamics.