Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning
Frank G. Glavin, Michael G. Madden

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…
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