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

arXiv: https://arxiv.org/abs/2405.11835 · HTML: https://mlsanity.com/paper/2405.11835/

## Key facts

- Cited by 1 papers in the map (data through 14 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.

## Placed in

- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)

## Builds on (most cited first, 2 of 2)

- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Game Agent Driven by Free-Form Text Command: Using LLM-based Code Generation and Behavior Branch](https://mlsanity.com/paper/2402.07442/) (2024)

## Built on by (most cited first, 1 of 1)

- [Real-Time World Crafting: Generating Structured Game Behaviors from Natural Language with Large Language Models](https://mlsanity.com/paper/2510.16952/) (2025)

## 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.