ResearchResearch paperReinforcement Learning · Speech & Audio · Agents & Tool Use1 source · Oct 20, 2026

Reusing Multimodal Agents: Efficient Evaluation of Sound Designs in Games

This paper introduces a novel framework for efficiently evaluating in-game sound designs using multimodal learning and reinforcement learning.

Sound design significantly influences gameplay and accessibility; however, objective evaluation methods, defined here as automated assessments based on agent performance rather than human ratings, often require retraining agents from scratch for each sound design.
A reinforcement learning agent is then trained using this latent representation.
We fine-tune the multimodal encoder for each sound design while keeping the trained policy fixed, allowing us to isolate the effects of sound changes on agent behavior.
To evaluate the proposed method, we conducted experiments on games from multiple genres, comparing the performance of blind agents (agents using only sound input) under various sound designs.

[1]Reusing Multimodal Agents: Efficient Evaluation of Sound Designs in Games
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:36 AM
This paper introduces a novel framework for efficiently evaluating in-game sound designs using multimodal learning and reinforcement learning.
Sound design significantly influences gameplay and accessibility; however, objective evaluation methods, defined here as automated assessments based on agent performance rather than human ratings, often require retraining agents from scratch for each sound design.

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