Training Interactive Agent in Large FPS Game Map with Rule-enhanced Reinforcement Learning

TL;DR

This paper presents PMCA, a reinforcement learning-based AI for large-scale FPS games, combining navigation mesh and rule-based shooting to improve navigation, combat, and human-like behaviors in complex multiplayer environments.

Contribution

The paper introduces NSRL, a novel method integrating navigation mesh and shooting rules with deep reinforcement learning for practical FPS game AI deployment.

Findings

Enhanced navigation and combat capabilities in large FPS maps.

AI behaviors closely mimic human players.

Effective integration of rule-based and learning-based methods.

Abstract

In the realm of competitive gaming, 3D first-person shooter (FPS) games have gained immense popularity, prompting the development of game AI systems to enhance gameplay. However, deploying game AI in practical scenarios still poses challenges, particularly in large-scale and complex FPS games. In this paper, we focus on the practical deployment of game AI in the online multiplayer competitive 3D FPS game called Arena Breakout, developed by Tencent Games. We propose a novel gaming AI system named Private Military Company Agent (PMCA), which is interactable within a large game map and engages in combat with players while utilizing tactical advantages provided by the surrounding terrain. To address the challenges of navigation and combat in modern 3D FPS games, we introduce a method that combines navigation mesh (Navmesh) and shooting-rule with deep reinforcement learning (NSRL). The integration of Navmesh enhances the agent's global navigation capabilities while shooting behavior is controlled using rule-based methods to ensure controllability. NSRL employs a DRL model to predict when to enable the navigation mesh, resulting in a diverse range of behaviors for the game AI. Customized rewards for human-like behaviors are also employed to align PMCA's behavior with that of human players.