LLM-Guided Reinforcement Learning for Adaptive NPC Behavior in Multi-Agent Combat Games
Scripted and rule-based non-player characters (NPCs) in combat video games often exhibit predictable behaviors that experienced players can exploit, while reinforcement learning (RL) agents typically retain a fixed policy after training and cannot readily adapt their strategy to different opponents. We investigate a runtime strategy-selection framework in which a large language model (LLM) guides a trained RL policy without modifying its underlying behavior. To demonstrate this, we train five NPC agents with a shared PPO policy in Unity and compare a baseline configuration, in which the policy acts independently, with an LLM-augmented configuration in which a locally hosted Mistral 7B mod...
| Authors | Hrithika Deepu Nair, Kayvan Karim |
|---|---|
| Primary category | cs.MA |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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