Curiosity Driven Multi-agent Reinforcement Learning for 3D Game Testing

TL;DR

This paper introduces cMarlTest, a multi-agent reinforcement learning approach that uses curiosity-driven exploration to improve 3D game testing efficiency and coverage over single-agent methods.

Contribution

The paper presents a novel multi-agent RL framework for 3D game testing that outperforms single-agent approaches in coverage and efficiency.

Findings

cMarlTest achieves higher coverage across multiple game levels.

cMarlTest is more time-efficient than single-agent RL methods.

Multi-agent collaboration improves testing effectiveness.

Abstract

Recently testing of games via autonomous agents has shown great promise in tackling challenges faced by the game industry, which mainly relied on either manual testing or record/replay. In particular Reinforcement Learning (RL) solutions have shown potential by learning directly from playing the game without the need for human intervention. In this paper, we present cMarlTest, an approach for testing 3D games through curiosity driven Multi-Agent Reinforcement Learning (MARL). cMarlTest deploys multiple agents that work collaboratively to achieve the testing objective. The use of multiple agents helps resolve issues faced by a single agent approach. We carried out experiments on different levels of a 3D game comparing the performance of cMarlTest with a single agent RL variant. Results are promising where, considering three different types of coverage criteria, cMarlTest achieved higher coverage. cMarlTest was also more efficient in terms of the time taken, with respect to the single agent based variant.