Room Environment v3 Introduces Temporal Knowledge Graph Memory for Agent Research

Global: Room Environment v3 Introduces Temporal Knowledge Graph Memory for Agent Research

Researchers behind a recent arXiv preprint have unveiled a configurable simulation called the Room Environment v3, designed to test agents that must retain and reason over information in partially observable settings. The environment, released in August 2024, models its hidden state as an RDF knowledge graph and supplies agents with observations formatted as RDF triples, enabling a direct link between perception and structured memory.

Environment Overview

The Room Environment v3 allows users to adjust grid dimensions, the number of rooms, internal walls, and the presence of moving objects. By representing the underlying world as a semantic graph, the platform offers a natural testbed for agents that need to integrate observations over time, mirroring real‑world scenarios where state evolves in a graph‑like manner.

Temporal Knowledge Graph Memory

To support long‑term storage, the authors propose a lightweight temporal knowledge graph (TKG) memory that augments RDF triples with RDF‑star‑style qualifiers such as time_added, last_accessed, and num_recalled. These annotations give agents the ability to track when facts were incorporated, how recently they were accessed, and how often they have been recalled, providing a structured mechanism for temporal reasoning.

Baseline Agents

The study evaluates several symbolic baselines that explicitly maintain and query the TKG under varying capacity limits, alongside two neural sequence models—a Long Short‑Term Memory network and a Transformer—that operate without an explicit graph structure. All agents are trained on a single layout configuration before being tested on a held‑out layout that shares the same dynamics but presents queries in a different order.

Evaluation Protocol

By keeping the underlying dynamics constant while altering query sequences, the authors expose a train‑test generalization gap. Performance is measured using question‑answer accuracy, allowing a direct comparison of how well each approach adapts to unseen query orders.

Key Findings

Results indicate that incorporating temporal qualifiers yields more stable performance across test conditions. Notably, the symbolic TKG agent achieves roughly four times higher test QA accuracy than the neural baselines when operating under identical environmental and query constraints, highlighting the advantage of explicit graph‑structured memory in this setting.

Resources and Availability

The authors have made the environment, agent implementations, and experimental scripts publicly available on GitHub at github.com/humemai/agent-room-env-v3 and github.com/humemai/room-env, facilitating reproducible research and further exploration of temporal knowledge graph methods.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.