We report promising results and envision that this method can be used to automate the process of LOD reduction testing and validation.
In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible.
This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments.
General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data.
As modern games continue growing both in size and complexity, it has become more challenging to ensure that all the relevant content is tested and that any potential issue is properly identified and fixed.
We present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with an auxiliary input as a control variable.
This initial training technique kick-starts TD learning and the agent quickly learns to surpass the capabilities of the expert.