A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
Serious Games (SGs) are nowadays shifting focus to include procedural content generation (PCG) in the development process as a means of offering personalized and enhanced player experience. However, the development of a framework to assess the impact of PCG techniques when integrated into SGs remains particularly challenging. This study proposes a methodology for automated evaluation of PCG integration in SGs, incorporating deep reinforcement learning (DRL) game testing agents. To validate the proposed framework, a previously introduced SG featuring card game mechanics and incorporating three different versions of PCG for nonplayer character (NPC) creation has been deployed. Version 1 features random NPC creation, while versions 2 and 3 utilize a genetic algorithm approach. These versions are used to test the impact of different dynamic SG environments on the proposed framework's agents. The obtained results highlight the superiority of the DRL game testing agents trained on Versions 2 and 3 over those trained on Version 1 in terms of win rate (i.e. number of wins per played games) and training time. More specifically, within the execution of a test emulating regular gameplay, both Versions 2 and 3 peaked at a 97% win rate and achieved statistically significant higher (p=0009) win rates compared to those achieved in Version 1 that peaked at 94%. Overall, results advocate towards the proposed framework's capability to produce meaningful data for the evaluation of procedurally generated content in SGs.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 47 references that are in the map. First posted on arXiv 22 May 2025.
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12 of 47 references.
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- 2024Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning13 Aug
Other references · 11
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2020Deep Learning for Procedural Content Generationcited by 73
- 2018General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithmscited by 45
- 2021Augmenting Automated Game Testing with Deep Reinforcement Learningcited by 38
- 2018Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristicscited by 28
- 2020Machine versus Human Attention in Deep Reinforcement Learning Taskscited by 16
- 2022Automated Play-Testing Through RL Based Human-Like Play-Styles Generationcited by 11
- 2020Predicting Game Difficulty and Churn Without Playerscited by 9
- 2022Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigationcited by 7
- 2020Capturing Local and Global Patterns in Procedural Content Generation via Machine Learningcited by 4
- 2023Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Gamescited by 2
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- 2024Playable Game Generation
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- 2026Augmenting Game AI with Deep Reinforcement Learning
- 2022Generative Personas That Behave and Experience Like Humans