Papers › Learning Knowledge Graph-based World Models of Textual Environments
Learning Knowledge Graph-based World Models of Textual Environments
Prithviraj Ammanabrolu, Mark O. Riedl
World models improve a learning agent's ability to efficiently operate in interactive and situated environments. This work focuses on the task of building world models of text-based game environments. Text-based games, or interactive narratives, are reinforcement learning environments in which agents perceive and interact with the world using textual natural language. These environments contain long, multi-step puzzles or quests woven through a world that is filled with hundreds of characters, locations, and objects. Our world model learns to simultaneously: (1) predict changes in the world caused by an agent's actions when representing the world as a knowledge graph; and (2) generate the set of contextually relevant natural language actions required to operate in the world. We frame this task as a Set of Sequences generation problem by exploiting the inherent structure of knowledge graphs and actions and introduce both a transformer-based multi-task architecture and a loss function to train it. A zero-shot ablation study on never-before-seen textual worlds shows that our methodology significantly outperforms existing textual world modeling techniques as well as the importance of each of our contributions.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Parsing | JerichoWorld | Worldformer | Set accuracy | 23.22 | #1 of 3 | Archive leaderboard | report |
| Action Parsing | JerichoWorld | CALM | Set accuracy | 13.79 | #3 of 3 | Archive leaderboard | report |
| Knowledge Graphs | JerichoWorld | Worldformer | Set accuracy | 39.15 | #1 of 5 | Archive leaderboard | report |
| Knowledge Graphs | JerichoWorld | GATA-W | Set accuracy | 24.06 | #3 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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