mlsanityData to 7 Oct 2026

Demo Paper: A Game Agents Battle Driven by Free-Form Text Commands Using Code-Generation LLM

arXiv 2405.11835 ↗First posted 20 May 2024cs.HCcited by 1

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

This paper presents a demonstration of our monster battle game, in which the game agents fight in accordance with their player's language commands. The commands were translated into the knowledge expression called behavior branches by a code-generation large language model. This work facilitated the design of the commanding system more easily, enabling the game agent to comprehend more various and continuous commands than rule-based methods. The results of the commanding and translation process were stored in a database on an Amazon Web Services server for more comprehensive validation. This implementation would provide a sufficient evaluation of this ongoing work, and give insights to the industry that they could use this to develop their interactive game agents.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 2 of its 6 references that are in the map. First posted on arXiv 20 May 2024.

Builds on

2 of 6 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Game Agent Driven by Free-Form Text Command: Using LLM-based Code Generation and Behavior Branch12 Feb

Other references · 1

  1. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,172

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Real-Time World Crafting: Generating Structured Game Behaviors from Natural Language with Large Language Modelscited by 1

Similar, not cited

Close in wording, no citation either way

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