Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning

TL;DR

This paper presents the Skilled Experience Catalogue, a novel skill-balancing mechanism for NPCs in FPS games that dynamically adjusts their proficiency using stored learning policies to match player skill levels.

Contribution

It introduces a new skill-balancing approach that uses a catalogue of pre-trained policies to adapt NPC skill levels in real-time during gameplay.

Findings

Effective in balancing NPC skill levels in FPS games

Allows real-time adjustment of NPC proficiency

Improves game challenge and player experience

Abstract

In this paper, we introduce a skill-balancing mechanism for adversarial non-player characters (NPCs), called Skilled Experience Catalogue (SEC). The objective of this mechanism is to approximately match the skill level of an NPC to an opponent in real-time. We test the technique in the context of a First-Person Shooter (FPS) game. Specifically, the technique adjusts a reinforcement learning NPC's proficiency with a weapon based on its current performance against an opponent. Firstly, a catalogue of experience, in the form of stored learning policies, is built up by playing a series of training games. Once the NPC has been sufficiently trained, the catalogue acts as a timeline of experience with incremental knowledge milestones in the form of stored learning policies. If the NPC is performing poorly, it can jump to a later stage in the learning timeline to be equipped with more informed decision-making. Likewise, if it is performing significantly better than the opponent, it will jump to an earlier stage. The NPC continues to learn in real-time using reinforcement learning but its policy is adjusted, as required, by loading the most suitable milestones for the current circumstances.