mlsanityData to 7 Oct 2026

Go-Explore Complex 3D Game Environments for Automated Reachability Testing

arXiv 2209.00570 ↗First posted 1 Sep 2022cs.AI cs.LG cs.SEcited by 3

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Abstract (arXiv)

Modern AAA video games feature huge game levels and maps which are increasingly hard for level testers to cover exhaustively. As a result, games often ship with catastrophic bugs such as the player falling through the floor or being stuck in walls. We propose an approach specifically targeted at reachability bugs in simulated 3D environments based on the powerful exploration algorithm, Go-Explore, which saves unique checkpoints across the map and then identifies promising ones to explore from. We show that when coupled with simple heuristics derived from the game's navigation mesh, Go-Explore finds challenging bugs and comprehensively explores complex environments without the need for human demonstration or knowledge of the game dynamics. Go-Explore vastly outperforms more complicated baselines including reinforcement learning with intrinsic curiosity in both covering the navigation mesh and number of unique positions across the map discovered. Finally, due to our use of parallel agents, our algorithm can fully cover a vast 1.5km x 1.5km game world within 10 hours on a single machine making it extremely promising for continuous testing suites.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 45 references that are in the map. First posted on arXiv 1 Sep 2022.

Builds on

19 of 45 references.

References · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2018Exploration by Random Network Distillationcited by 966
  5. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 632
  6. 2016#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learningcited by 307
  7. 2020First return, then explorecited by 256
  8. 2017Count-Based Exploration with Neural Density Modelscited by 255
  9. 2019Go-Explore: a New Approach for Hard-Exploration Problemscited by 207
  10. 2018Semi-parametric Topological Memory for Navigationcited by 203
  11. 2021Augmenting Automated Game Testing with Deep Reinforcement Learningcited by 38
  12. 2021Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agentscited by 28
7 more
  1. 2019Automated Video Game Testing Using Synthetic and Human-Like Agentscited by 22
  2. 2021A Survey of Video Game Testingcited by 18
  3. 2022Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigationcited by 7
  4. 2018Taking the Scenic Route: Automatic Exploration for Videogamescited by 6
  5. 2022CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectoriescited by 6
  6. 2021We'll Fix It in Post: What Do Bug Fixes in Video Game Update Notes Tell Us?cited by 5
  7. 2022Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Treescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.