mlsanityData to 7 Oct 2026

CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories

arXiv 2202.10057 ↗First posted 21 Feb 2022cs.LG cs.AIcited by 6

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Abstract (arXiv)

This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments. The Curiosity-Conditioned Proximal Trajectories (CCPT) method combines curiosity and imitation learning to train agents to methodically explore in the proximity of known trajectories derived from expert demonstrations. We show how CCPT can explore complex environments, discover gameplay issues and design oversights in the process, and recognize and highlight them directly to game designers. We further demonstrate the effectiveness of the algorithm in a novel 3D navigation environment which reflects the complexity of modern AAA video games. Our results show a higher level of coverage and bug discovery than baselines methods, and it hence can provide a valuable tool for game designers to identify issues in game design automatically.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 21 Feb 2022.

Builds on

18 of 26 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
  5. 2017Hindsight Experience Replaycited by 1,096
  6. 2016Least Squares Generative Adversarial Networkscited by 1,000
  7. 2018Exploration by Random Network Distillationcited by 966
  8. 2018Which Training Methods for GANs do actually Converge?cited by 672
  9. 2016ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learningcited by 178
  10. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  11. 2019A survey on intrinsic motivation in reinforcement learningcited by 79
  12. 2021Adversarial Reinforcement Learning for Procedural Content Generationcited by 36
6 more
  1. 2018Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristicscited by 28
  2. 2021Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agentscited by 28
  3. 2020Deep Reinforcement Learning for Navigation in AAA Video Gamescited by 27
  4. 2021AMP: Adversarial Motion Priors for Stylized Physics-Based Character Controlcited by 18
  5. 2021Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigationcited by 16
  6. 2019Automated Playtesting of Matching Tile Gamescited by 10

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 3.

6 papers.

  1. 2022Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learningcited by 13
  2. 2023Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Gamescited by 12
  3. 2022Automated Play-Testing Through RL Based Human-Like Play-Styles Generationcited by 11
  4. 2023Improving Generalization in Game Agents with Data Augmentation in Imitation Learningcited by 4
  5. 2022Go-Explore Complex 3D Game Environments for Automated Reachability Testingcited by 3
  6. 2023Policy Diversity for Cooperative Agentscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Adapting a World Model for Trajectory Following in a 3D Game
  2. 2022Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation
  3. 2019Go-Explore: a New Approach for Hard-Exploration Problems
  4. 2020Guided Exploration with Proximal Policy Optimization using a Single Demonstration
  5. 2018Imitation Learning with Concurrent Actions in 3D Games
  6. 2026Valerant: An Automatic Navigable Game Map Generator via Action-Conditioned World Model Exploration
  7. 2023Preference-conditioned Pixel-based AI Agent For Game Testing
  8. 2025Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration